Tracking Dynamics of Topic Trends Using a Finite Mixture Model In a wide range of business areas dealing with text data streams , including CRM , knowledge management , and Web monitoring services , it is an important issue to discover topic trends and analyze their dynamics in real-time . Specifically we consider the following three tasks in topic trend analysis : 1 -RRB- Topic Structure Identification ; identifying what kinds of main topics exist and how important they are , 2 -RRB- Topic Emergence Detection ; detecting the emergence of a new topic and recognizing how it grows , 3 -RRB- Topic Characterization ; identifying the characteristics for each of main topics . For real topic analysis systems , we may require that these three tasks be performed in an on-line fashion rather than in a retrospective way , and be dealt with in a single framework . This paper proposes a new topic analysis framework which satisfies this requirement from a unifying viewpoint that a topic structure is modeled using a finite mixture model and that any change of a topic trend is tracked by learning the finite mixture model dynamically.In this framework we propose the usage of a time-stamp based discounting learning algorithm in order to realize real-time topic structure identification . This enables tracking the topic structure adaptively by forgetting out-of-date statistics.Further we apply the theory of dynamic model selection to detecting changes of main components in the finite mixture model in order to realize topic emergence detection.We demonstrate the effectiveness of our framework using real data collected at a help desk to show that we are able to track dynamics of topic trends in a timely fashion . H. 2.8 -LSB- Database Management -RSB- : Database Applications Data Mining Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page . To copy otherwise , to republish , to post on servers or to redistribute to lists , requires prior specific permission and/or a fee . KDD ' 04 , August 22 25 , 2004 , Seattle , Washington , USA . Copyright 2004 ACM 1-58113-888-1 / 04/0008 ... $ 5.00 . In a wide range of business areas dealing with text streams , including CRM , knowledge management , and Web monitoring services , it is an important issue to discover topic trends and analyze their dynamics in real-time . For example , it is desired in the CRM area to grasp a new trend of topics in customers ' claims every day and to track a new topic as soon as it emerges.A topic is here defined as a seminal event or activity.Specifically we consider the following three tasks in topic analysis : 1 -RRB- Topic Structure Identification ; learning a topic structure in a text stream , in other words , identifying what kinds of main topics exist and how important they are . 2 -RRB- Topic Emergence Detection ; detecting the emergence of a new topic and recognizing how rapidly it grows , similarly , detecting the disappearance of an existing topic . 3 -RRB- Topic Characterization ; identifying the characteristics for each of main topics . For real topic analysis systems , we may require that these three tasks be performed in an on-line fashion rather than in a retrospective way , and be dealt with in a single framework . The main purpose of this paper is to propose a new topic analysis framework that satisfies the requirement as above , and to demonstrate its effectiveness through its experimental evaluations for real data sets . Our framework is designed from a unifying viewpoint that a topic structure in a text stream is modeled using a finite mixture model -LRB- a model of the form of a weighted average of a number of probabilistic models -RRB- and that any change of a topic trend is tracked by learning the finite mixture model dynamically.Here each topic corresponds to a single mixture component in the model . All of the tasks 1 -RRB- -3 -RRB- are formalized in terms of a finite mixture model as follows : As for the task 1 -RRB- , the topic structure is identified by statistical parameters of a finite mixture model.They are learned using our original time-stamp based discounting learning algorithm , which incrementally and adaptively estimates statistical parameters of the model by gradually forgetting out-of-date statistics , making use of time-stamps of data.This makes the learning procedure adaptive to changes of the nature of text streams . As for the task 2 -RRB- , any change of a topic structure is rec-ognized by tracking the change of main components in a mixture model.We apply the theory of dynamic model selection -LSB- 7 -RSB- to detecting changes of the optimal number of 811 Industry/Government Track Poster main components and their organization in the finite mixture model.We may recognize that a new topic has emerged if a new mixture component is detected in the model and remains for a while.Unlike conventional approaches to statistical model selection under the stationary environment , dynamic model selection is performed under the non-stationary one in which the optimal model may change over time.Further note that we deal with a complicated situation where the dimension of input data , i.e. , the number of features of a text vector , may increase as time goes by . As for the task 3 -RRB- , we classify every text into the cluster for which the posterior probability is largest , and then we characterize each topic using feature terms characterizing texts classified into its corresponding cluster.These feature terms are extracted as those of highest information gain , which are computed in real-time . We demonstrate the validity of the topic trend analysis framework , by showing experimental results on its applications to real domains.Specifically we emphasize that it is really effective for discovering trends in questions at a help desk . 1.2 Related Work The technologies similar to 1 -RRB- -3 -RRB- have extensively been ex-plored in the area of topic detection and tracking -LRB- TDT -RRB- -LRB- see -LSB- 1 -RSB- -RRB- . Actually 1 -RRB- and 2 -RRB- are closely related to the subprob-lems in TDT called topic tracking and new event detection , respectively.Here topic tracking is to classify texts into one of topics specified by a user , while new event detection , formerly called first story detection , is to identify texts that discuss a topic that has not already been reported in earlier texts.The latter problem is also related to work on topic-conditioned novelty detection by Yang et.al . -LSB- 16 -RSB- . In most of related TDT works , however , topic tracking or new event detection is conducted without identifying main topics or a topic structure , hence the tasks 1 -RRB- -3 -RRB- can not be unified within a conventional TDT framework.Further topic timeline analysis has not been addressed in it . Swan and Allen -LSB- 12 -RSB- addressed the issue of how to auto-matically overview timelines of a set of news stories.They used the 2 - method to identify at each time a burst of feature terms that more frequently appear than at other times . Similar issues are addressed in the visualization community -LSB- 3 -RSB- . However , all of the methods proposed there are not designed to perform in an on-line fashion . Kleinberg -LSB- 4 -RSB- proposed a formal model of `` bursts of ac-tivity '' using an infinite-state automaton.This is closely related to topic emergence detection in our framework.A burst has a somewhat different meaning from a topic in the sense that the former is a series of texts including a specific feature , while the latter is a cluster of categorized texts.Hence topic structure identification and characterization can not be dealt with in his model.Further note that Kleinberg 's model is not designed for real-time analysis but for retrospective one . Related to our statistical modeling of a topic structure , Liu et.al . -LSB- 2 -RSB- and Li and Yamanishi -LSB- 6 -RSB- also proposed methods for topic analysis using a finite mixture model.Specifically , Liu et.al . considered the problem of selecting the optimal number of mixture components in the context of text clustering.In their approach a single model is selected as an optimal model under the assumption that the optimal model does not change over time.Meanwhile , in our approach , a sequence of optimal models is selected dynamically under the assumption that the optimal model may change over time . Related to topic emergence detection , Matsunaga and Yamanishi -LSB- 7 -RSB- proposed a basic method of dynamic model selection , by which one can dynamically track the change of number of components in the mixture model.However , any of all of these technologies can not straightforwardly be applied to real-time topic analysis in which the dimension of data may increase as time goes by . Related to topic structure identification , an on-line discounting learning algorithm for estimating parameters in a finite mixture model has been proposed by Yamanishi et . al. -LSB- 14 -RSB- . The main difference between our algorithm and theirs is that the former makes use of time-stamps in order to make the topic structure affected by a timeline of topics while the latter considers only the time-order of data ignoring their time-stamps . The rest of this paper is organized as follows : Section 2 describes a basic model of topic structure.Section 3 gives a method for topic structure identification.Section 4 gives a method for topic emergence detection.Section 5 gives a method for topic characterization.Section 6 gives experimental results.Section 7 gives concluding remarks . We employ a probabilistic model called a finite mixture model for the representation of topic generation in a text stream.Let W = -LCB- w 1 , , w d -RCB- be the complete vocabulary set of the document corpus after the stop-words removal and words stemming operations.For a given document x , let tf -LRB- w i -RRB- be the term frequency of word w i in x.Let idf -LRB- w i -RRB- be the idf value of w i , i. e. , idf -LRB- w i -RRB- = log -LRB- N/df -LRB- w i -RRB- -RRB- where N is the total number of texts for reference and df -LRB- w i -RRB- is the frequency of texts in which w i appears.Let tf-idf -LRB- w i -RRB- be the tf-idf value of w i in x , i. e. , tf-idf -LRB- w i -RRB- = tf -LRB- w i -RRB- log -LRB- N/df -LRB- w i -RRB- -RRB- . We may represent a text x of the form : x = -LRB- tf -LRB- w 1 -RRB- , ... , tf -LRB- w d -RRB- -RRB- or x = -LRB- tf-idf -LRB- w 1 -RRB- , ... , tf - idf -LRB- w d -RRB- -RRB- . We may use either type of the representation forms . Let K be a given positive integer representing the number of different topics.We suppose that a text has only one topic and a text having the i-th topic is distributed according to the probability distribution with a density : p i -LRB- x | i -RRB- -LRB- i = 1 , 2 , , K -RRB- , where i is a real-valued parameter vector.We suppose here that x is distributed according to a finite mixture distribution -LRB- see e.g. , -LSB- 8 -RSB- -RRB- with K components given by p -LRB- x | : K -RRB- = K X i = 1 i p -LRB- x | i -RRB- , -LRB- 1 -RRB- where i > 0 -LRB- i = 1 , , K -RRB- and P K i = 1 i = 1 . We set = -LRB- 1 , , K-1 , 1 , , K -RRB- . Here i denotes the degree to what the i-th topic is likely to appear in a text stream . Note that each component in the mixture defines a single cluster in the sense of soft-clustering . Throughout this paper we suppose that each p i -LRB- x | i -RRB- takes a form of a Gaussian density : Letting d be the dimension of 812 Industry/Government Track Poster Text Data Stream Discount Learning Finite Mixture Model 1 Dynamic Model Selection Topic Emergence Detection Topic Characteriztion Timeline of Topics Discount Learning Discount Learning Finite Mixture Model 2 Finite Mixture Model K Figure 1 : Topic Trend Analysis System each datum , p i -LRB- x | i -RRB- = i -LRB- x | i , i -RRB- -LRB- 2 -RRB- = 1 -LRB- 2 -RRB- d/2 | i | 1/2 exp , , - 12 -LRB- x - i -RRB- T -1 i -LRB- x - i -RRB- , where i is a d-dimensional real-valued vector , i is a d d-dimensional matrix , and we set i = -LRB- i , i -RRB- . In this case -LRB- 1 -RRB- is so-called a Gaussian mixture.Note that a Gaussian density may be replaced with any other form of probability distributions , such as a multinomial distribution . In terms of a finite mixture model , a topic structure is identified by A -RRB- the number of components K -LRB- how many topics exist -RRB- , B -RRB- the weight vector -LRB- 1 , , K -RRB- indicating how likely each topic appears , and C -RRB- the parameter values i -LRB- i = 1 , , K -RRB- indicating how each topic is distributed.A topic structure in a text stream must be learned in an on-line fashion.Topic emergence detection is conducted by tracking the change of main components in the mixture model . Topic characterization is conducted by classifying each text into the component for which the posterior is largest and then by extracting feature terms characterizing the classified texts.Topic drift may be detected by tracking changes of a parameter value i for each topic i.These tasks will be described in details in the sessions to follow . The overall flow of the tasks is illustrated in Figure 1 . A text is sequentially input to the system.We prepare a number of finite mixture models , for each of which we learn statistical parameters using the time-stamp based learning algorithm to perform topic identification.These tasks are performed in parallel.On the basis of the input data and learned models , we conduct dynamic model selection for choosing the optimal finite mixture model.We then compare the new optimal model with the last one to conduct topic emergence detection.Finally for each component of the optimal model , we conduct topic characterization . In this section we propose an algorithm for learning a topic structure , which we call an time-stamp based discounting topic learning algorithm . The algorithm is basically designed as a variant of the incremental EM algorithm for learning a finite mixture model -LRB- see , e.g. , Neal and Hinton -LSB- 9 -RSB- -RRB- . Our proposed one is distinguished from existing ones with regards to the following three main features : 1 -RRB- Adaptive to the change of the topic structure . The parameters are updated by forgetting out-of-date statistics as time goes on.This is realized by putting a larger weight to the statistics for a more recent data . 2 -RRB- Making use of time stamps for texts . Not only the time order of texts but also their time stamps are utilized to make the topic structure depend on the timeline.For example , for two text data x t 1 , x t 2 -LRB- t 1 < t 2 -RRB- , if the length t 2 - t 1 is larger , the topic structure learned at time t 2 will be less affected by that at time t 1 . 3 -RRB- Normalizing data of different dimensions . We consider the on-line situation where the dimension of a datum may increase as time goes by.This situation actually occurs because new words may possibly be added to the list of words every time a new text is input.Hence it is needed for normalizing data of different dimensions . We suppose that text data x 1 , x 2 , ... are given in this order , and each has a time-stamp indicating when it appeared . Here is a description of the algorithm , in which is a discounting parameter , i denotes the posterior density of the ith component , and m is introduced for calculation of weights for old statistics . Time-stamp Based Discounting Learning Algorithm Initialization : Set initial values of -LRB- 0 -RRB- i , -LRB- 0 -RRB- i , -LRB- 0 -RRB- i , m -LRB- 0 -RRB- -LRB- i = 1 , , k -RRB- . Let > 0 , 0 < < 1 be given . Iteration : For t = 1 , 2 , . . do the following procedure . For the t-th data be x t and its time stamp be t new . Let the time stamp of the -LRB- t - 1 -RRB- - th data be t old . For i = 1 , , k , update the parameters according to the 813 Industry/Government Track Poster following rules : p -LRB- i | x t -RRB- : = -LRB- t-1 -RRB- i p i -LRB- x t | -LRB- t-1 -RRB- i , -LRB- t-1 -RRB- i -RRB- P k l = 1 -LRB- t-1 -RRB- l p l -LRB- x t | -LRB- t-1 -RRB- l , -LRB- t-1 -RRB- l -RRB- -LRB- t -RRB- i : = WA -LRB- p -LRB- i | x t -RRB- , 1/k | 1 , -RRB- -LRB- t -RRB- i : = WA -LRB- -LRB- t -RRB- i , -LRB- t -RRB- i | m -LRB- t-1 -RRB- , - -LRB- t new - t old -RRB- -RRB- -LRB- t -RRB- i : = WA -LRB- -LRB- t-1 -RRB- i , x t | i m -LRB- t-1 -RRB- , - -LRB- t new - t old -RRB- -LRB- t -RRB- i -RRB- -LRB- t -RRB- i : = WA -LRB- -LRB- t-1 -RRB- i , x i x T i | i m -LRB- t-1 -RRB- , - -LRB- t new - t old -RRB- -LRB- t -RRB- i -RRB- -LRB- t -RRB- i : = -LRB- t -RRB- i - i T i m -LRB- t -RRB- : = -LRB- t new - t old -RRB- m -LRB- t-1 -RRB- + 1 , where WA denotes the operation such that WA -LRB- X , Y | A , B -RRB- = A A + B X + B A + B Y. Generally , we set the initial value -LRB- 0 -RRB- i = 1/K , m -LRB- 0 -RRB- = 0 , a small value to -LRB- 0 -RRB- i , and set -LRB- 0 -RRB- i the first x t s that are different each other.This algorithm updates i , i , and i as the weighted average of the latest parameter value and the new statistics.The weight ratio is m -LRB- t-1 -RRB- : - -LRB- t new - t old -RRB- for i , and i m -LRB- t-1 -RRB- : - -LRB- t new - t old -RRB- -LRB- t -RRB- i for i and i , respectively . Note that Yamanishi et.al . 's sequentially discounting learning algorithm -LSB- 14 -RSB- can be thought of as a special case of this algorithm in which the time interval t l +1 - t l is independent of l.In that case if we further let = 1 , the algorithm becomes an ordinary incremental EM algorithm . In real implementation , we supposed that i is a diagonal matrix for the sake of computational complexity issues.The scalability issue for dealing with a general matrix i remains for future study . In this section we are concerned with the issue of topic emergence detection , i.e. , tracking the emergence of a new topic.We reduce here this issue to that of selecting the optimal components in the mixture model dynamically.We call this statistical issue dynamic model selection -LRB- see also -LSB- 7 -RSB- -RRB- . The key idea of dynamic model selection is to first learn a finite mixture model with a relatively large number of components , then to select main components dynamically from among them on the basis of Rissanen 's predictive stochastic complexity -LSB- 10 -RSB- . The procedure of dynamic model selection is described as follows : Initialization : Let K max -LRB- maximum number of mixture components -RRB- and W -LRB- window size -RRB- be given positive integers . Set initial values of -LRB- 0 -RRB- i , -LRB- 0 -RRB- i = -LRB- -LRB- 0 -RRB- i , -LRB- 0 -RRB- i -RRB- -LRB- i = 1 , , K max -RRB- . Iteration : For t = 1 , 2 , , do the following procedure 1 to 4 : 1 . Model Class Construction : Let G t i be the window average of the posterior probability -LRB- t-W i + + t i -RRB- / W . For k = 1 , , K max , do the following procedure : Let 1 , , k be the indices of k highest scores such that G -LRB- t-1 -RRB- 1 G -LRB- t-1 -RRB- k . Construct the following mixture model with k components : For s = t - W , , t , p -LRB- t-1 -RRB- -LRB- x | 1 , , k -RRB- = k-1 X j = 1 -LRB- t-1 -RRB- j p j -LRB- x | -LRB- t-1 -RRB- j -RRB- + 1 k -1 X j = 1 -LRB- t-1 -RRB- j ! U. where U is a uniform distribution over the domain . 2 . Predictive Stochastic Complexity Calculation : When the t-th input data x t with dimension d t is given , compute S -LRB- t -RRB- -LRB- k -RRB- = t X s = t-W '' - logp -LRB- s -RRB- -LRB- x s | 1 , , k -RRB- / d s '' . -LRB- 3 -RRB- 3 . Model Selection : Select k t minimizing S -LRB- t -RRB- -LRB- k -RRB- . Let p j -LRB- x | -LRB- t-1 -RRB- j -RRB- -LRB- j = 1 , , k t -RRB- be main components at time t , which we write as -LCB- C -LRB- t -RRB- 1 , C -LRB- t -RRB- k t -RCB- . 4 . Estimation of Parameters : Learn a finite mixture model with K max components using the time-stamp based discounting learning algorithm.Let the estimated parameter be -LRB- -LRB- t -RRB- 1 , , -LRB- t -RRB- K max , -LRB- t -RRB- 1 , , -LRB- t -RRB- K max -RRB- . Note that the S -LRB- t -RRB- -LRB- k -RRB- can be thought of as a variant of Rissanen 's predictive stochastic complexity -LSB- 10 -RSB- normalized by the dimension for each datum , which can be interpreted as the total code length required for encoding a data stream x t-W , ... , x t into a binary string sequentially . Once main components C -LRB- t -RRB- 1 , , C -LRB- t -RRB- k t are obtained , we compare them with C -LRB- t-1 -RRB- 1 , , C -LRB- t-1 -RRB- k t-1 to check the emergence of a new topic or the disappearance of an existing topic in the following way.If a new component is selected at some point and remains for a longer time than a specified threshold , we may determine that a new topic has emerged . Specifically , if the optimal number k t of components becomes larger than k t-1 , we can recognize that a new topic has emerged.Similarly , if an existing component is not selected at some time and does not appear any longer , then we may determine that the topic has disappeared . Once the optimal finite mixture model is obtained , we are concerned with the issue of how to characterize each topic.We address this issue by extracting terms characterizing each topic and by observing the growth or decay of each topic component.Details are shown below . A -RRB- Extracting terms characterizing each topic . We attempt to characterize each topic by extracting characteristic words for it.We perform this task by computing the information gain of possible words . In the time-stamp based discounting topic learning algorithm , the posterior probability distribution over the set of clusters is estimated every time a text data is input.According to that posterior distribution an input text will be 814 Industry/Government Track Poster categorized into the component for which the posterior probability is largest.This clustering task can be performed in an on-line fashion . After observing the t-th datum x t , for i = 1 , , k , let S t -LRB- i -RRB- be the set of texts in x t = x 1 , . . , x t classified into the i-th component and let t i be the size of S t -LRB- i -RRB- . Let S t = k i = 1 S t -LRB- i -RRB- . Below we show the method for computing the information gain of a term w for each topic component.For any term w , let S -LRB- w -RRB- be a set of vectors in S t such that the frequency of w is larger than a given threshold , and let m w be the size of S -LRB- w -RRB- . Let S -LRB- w -RRB- be a set of vectors in S t such that the frequency of w is not larger than the threshold , and m w be the size of S -LRB- w -RRB- . For a specified topic component , say , the i-th component , let m + w be the number of vectors in S -LRB- w -RRB- that are also included in S t -LRB- i -RRB- . Let m + w be the number of vectors in S -LRB- w -RRB- that are also included in S t -LRB- i -RRB- . Then we define the information gain of w for the i-th topic component as follows : IG -LRB- w | i -RRB- = I -LRB- t , t i -RRB- - ` I -LRB- m w , m + w -RRB- + I -LRB- m w , m + w -RRB- , where I -LRB- x , y -RRB- is the information measure such as stochastic complexity -LSB- 10 -RSB- , extended stochastic complexity -LSB- 13 -RSB- -LSB- 5 -RSB- . The stochastic complexity -LSB- 10 -RSB- is given as follows : I -LRB- x , y -RRB- = xH '' y x '' + 1 2 log '' x 2 '' , where H -LRB- x -RRB- = - x log x - -LRB- 1 - x -RRB- log -LRB- 1 - x -RRB- log -LRB- 1 - x -RRB- is the binary entropy function , and log 's base is 2 . A special case of extended stochastic complexity is given as follows -LSB- 13 -RSB- -LSB- 5 -RSB- : I -LRB- x , y -RRB- = min -LCB- y , x - y -RCB- + cpx log x , where c is a constant . We select a specified number of terms ws of largest information gains.We can think of them the set of terms characterizing the i-th topic component . The statistics : m w , m + w , m w , m + w needed for computing information gain can be calculated in an on-line fashion . Hence topic characterization task is conducted in real-time . B -RRB- Observing the growth or decay of each cluster . Let G -LRB- t -RRB- i be the window average of the posterior probability of the ith topic component , that is , G t i = -LRB- t-W i + , , + t i -RRB- / W . G -LRB- t -RRB- i increases when texts corresponding to the i-th topic is input , and decreases when the other is input.We can see how rapidly this topic grows by observing G -LRB- t -RRB- i as t goes by . We conducted an experiment on real data : contact data of a help desk for an internal e-mail service.An example of the data record is presented in Table 1 . It has the field of contact date/time , question/request , answered date/time , answer , and so on.The number of the records is 1202 . The date of the first/last contact is Feb 21 2004/May 20 respectively . We input contact dates as the time-stamps , and ques-tions/requests as the text data to our system.We set K max to 50 and to 0.99 . Our system ran on an NEC Express5800 with 1 GHz Pentium III and a 1 GB memory.The system was implemented using C , and OS was Windows 2000 Server . Processing 1202 records of data took about five minute . Figure 2 shows the number of components k t selected by our system as main topics . The number increases at the Date k * t Figure 2 : Number of main topics beginning of March , and has a peak in the middle of April . Since a fiscal year begins at April in Japan , we can suppose that the number of topics at the help desk is increasing around the first day of April . Let us look into a few of the components , because we do not have enough space for all of the components.Here , we observe Component 27 and 42 in detail.Figure 3 shows the window averages G 27 , G 42 of the posterior probabilities and the periods where the components are selected as main topics . G 27 increases in the beginning of April and has the first G 27 G 42 C27 is main . C42 is main . Figure 3 : G i and Period of Component 27 , 42 peak at April 12 . Then it repeats increase and decrease until the middle of May.The corresponding component is selected as main from the first week of April , and remains as main until the middle of May -LRB- with short discontinuances -RRB- . G 42 is positive during April , and also the corresponding topic is main during April.The lines of G i s indicate how important the corresponding topics are in each time.Moreover , we can observe how the emerged topics grows and disappears from the figure.The topic corresponding to Component 42 emerges at the beginning of April , grows for two weeks , is attenuated , then drops out from the main topics at the end of April . Term `` transfer '' was extracted as a characteristic word 815 Industry/Government Track Poster Table 1 : Examples of help desk data records Contact date/time Question/Request Answered date/time Answer , , , Feb 26 2004 14:05 I forgot my password . How can I ... Feb 26 2004 14:48 You can get a new ... Feb 26 2004 14:08 Until what time is an account for a ... Feb 26 2004 14:25 It is valid for 14 days after retirement . Feb 26 2004 14:09 Is it possible to forward mails from ... Feb 26 2004 15:09 Yes . You can set up by ... ... ... ... ... . for Component 27 . Texts classified into this component are questions like `` Is it possible to use Service XXX after I am transfered to YYY ? '' . That kind of questions may increase around the beginning of a fiscal year . `` Service ZZZ '' and `` failure '' were extracted as chracteristic words for Component 42 . Actually , Service ZZZ failed in the beginning of April , then , the topic consists of related complaints and questions . In this way we can recognize the emergence , growth , and decay of each topic from the system.Through this example it has turned out that our framework for topic trend analysis are very effective for tracking dynamics of topic trends in contact data at a help desk . In this paper we have proposed a framework for tracking dynamics of topic trends using a finite mixture model.In this framework the three main tasks : topic structure identification , topic emergence detection , and topic characterization are unified within a single framework.Topic structure identification has been realized by our unique time-stamp based learning algorithm.It enables tracking topic structures adaptively by forgetting out-of-date statistics.Topic emergence detection has been realized on the basis of the theory of dynamic model selection.It enables detecting changes of the optimal number of components in the finite mixture model to check whether a new topic has appeared or not.Topic characterization has been realized by on-line text clustering and feature extraction based on information gain.Through the experiments using real data collected at a help desk , it is demonstrated that our framework works well in the sense that dynamics of topic trends can be tracked in a timely fashion . The following issues remain open for future study : Context-based topic trend analysis : In this paper we have proposed an approach to word-based topic trend analysis . However , we need to further analyze contexts , i.e. , relations among words , in order to more deeply analyze the semantics of topics . Multi-topics analysis : We supposed that one text comes from a single mixture component corresponding to a single topic.It is our future study how to deal with texts having multi topics . -LSB- 1 -RSB- J.Allen , R.Papka , and V.Lavrenko : On-line new event detection and tracking , in Proceedings of SIGIR International Conference on Information Retrieval , pp : 37-45 , 1998 . -LSB- 2 -RSB- X.Liu , Y.Gong , W.Xu , and S.Zhu : Document clustering with cluster refinement and model selection capabilities , in Proceedings of SIGIR International Conference on Information Retrieval , pp : 191-198 , 2002 . -LSB- 3 -RSB- S.Harve , B.Hetzler , and L.Norwell : ThemeRiver : Visualizing theme changes over time , in Proceesings of IEEE Symposium on Information Visualization , pp : 115-123 , 2000 . -LSB- 4 -RSB- J.Kleiberg : Bursty and hierarchical structure in streams , in Proceedings of KDD2002 , pp : 91-101 , ACM Press , 2003 . -LSB- 5 -RSB- H.Li and K.Yamanishi : Text classification using ESC-based decision lists , Information Processing and Management , vol .38 / 3 , pp : 343-361 , 2002 . -LSB- 6 -RSB- H.Li and K.Yamanishi : Topic analysis using a finite mixture model , Information Processing and Management , Vol .39 / 4 , pp 521-541 , 2003 . -LSB- 7 -RSB- Y.Matsunaga and K.Yamanishi : An information-theoretic approach to detecting anomalous behaviors , in Information Technology Letters vol .2 -LRB- Proc . of the 2nd Forum on Information Technologies -RRB- , pp : 123-124 , -LRB- in Japanese -RRB- 2003 . -LSB- 8 -RSB- G.McLahlan and D.Peel : Finite Mixture Models , Wiley Series in Probability and Statistics , John Wiley and Sons , 2000 . -LSB- 9 -RSB- R.M.Neal and G.E.Hinton : A view of the EM algorithm that justifies incremental sparse , and other variants , Learning in Graphical Models , M. Jordan -LRB- editor -RRB- , MIT Press , Cambridge MA , USA . -LSB- 10 -RSB- J.Rissanen : Universal coding , information , and estimation , IEEE Trans . on Inform . Theory , 30:629-636 , 1984 . -LSB- 11 -RSB- R.Swan and J.Allen : Extracting significant time-varying features from text , in Proceedings of 8th International Conference on Information Knowledge Management , pp : 38-45 , 1999 . -LSB- 12 -RSB- R.Swan and J.Allen : Automatic generation of overview timelines , in Proceedings of SIGIR International Conference on Information Retrieval , pp : 49-56 , 2000 . -LSB- 13 -RSB- K.Yamanishi : A Decision-theoretic Extension of Stochastic Complexity and Its Applications to Learning , IEEE Trans . on Inform . Theory , vol .44 / 4 , pp : 1424-1439 , 1998 . -LSB- 14 -RSB- K.Yamanishi , J.Takeuchi , G.Williams , and P.Milne : On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms , '' in Proceedings of KDD2000 , ACM Press , pp : 320 324 2000 . -LSB- 15 -RSB- Y.Yang , T.Pierce , J.G.Carbonell : A study on retrospective and on-line event detection , in Proceedings of SIGIR International Conference on Information Retrieval , pp : 28-30 , 1998 . -LSB- 16 -RSB- Y.Yang , J.Zang , J.Carbonell , and C.Jin : Topic-conditioned novelty detection , in Proceedings of KDD 2002 , pp : 688-693 , 2002 . 816 Industry/Government Track Poster

B I O B I O O B I I O O O O O O O O O B I I O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O B I I O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I I O O O O O O B I O O O O O B I I O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I I O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O B I B I I O O O O O O O O O B I I O B I I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O B I O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O B I I O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

tracking dynamics
topic detection and tracking
text data streams
data mining
model selection
topic trend
topic analysis
finite mixture model
crm
topic structure identification
topic trends
topic characterization
information gain
topic emergence detection


A

Case

Study

on

How

to

Manage

the

Theft

of

Information

This

paper

shows

the

importance

that

management

plays

in

the

protection

of

information

and

in

the

planning

to

handle

a

security

breach

when

a

theft

of

information

happens

Recent

thefts

of

information

that

have

hit

major

companies

have

caused

concern

These

thefts

were

caused

by

companies

inability

to

determine

risks

associated

with

the

protection

of

their

data

and

these

companies

lack

of

planning

to

properly

manage

a

security

breach

when

it

occurs

It

is

becoming

necessary

if

not

mandatory

for

organizations

to

perform

ongoing

risk

analysis

to

protect

their

systems

Organizations

need

to

realize

that

the

theft

of

information

is

a

management

issue

as

well

as

a

technology

one

and

that

these

recent

security

breaches

were

mainly

caused

by

business

decisions

by

management

and

not

a

lack

of

technology

C.

-LSB-

Computer-Communication

Networks

-RSB-

General

Security

and

protection

K.

-LSB-

Computers

And

Education

-RSB-

Computer

and

Information

Science

Education

Curriculum

Information

systems

education

K.

-LSB-

Computers

and

Society

-RSB-

Public

Policy

Issues

Abuse

and

crime

involving

computers

Computer-related

health

issues

Ethics

Intellectual

property

right

Privacy

Social

Issues

Abuse

and

crime

involving

computers

Electronic

Commerce

Security

K.

-LSB-

Management

of

Computing

and

Information

Systems

-RSB-

Security

and

Protection

Authentication

Invasive

software

unauthorized

access

Management

Security

Human

Factors

Standardization

Legal

Aspects

After

counter-terrorism

and

counter-intelligence

cyber

crime

is

the

third

highest

priority

for

the

U.S.

Federal

Bureau

-LSB-

-RSB-

With

the

rise

of

the

theft

of

information

and

the

lure

of

big

profits

for

this

stolen

information

it

is

necessary

for

information

systems

to

have

the

ability

to

protect

this

valuable

asset

It

is

estimated

that

a

credit

card

number

unsupported

by

any

other

documentation

is

worth

and

a

credit

history

report

retails

for

-LSB-

-RSB-

Recent

breaches

of

information

systems

that

have

lead

to

thefts

of

information

have

shown

that

management

practices

and

not

technology

was

part

of

the

issue

and

in

some

cases

the

primary

cause

of

the

theft

of

the

information

With

each

of

these

thefts

there

is

a

third

party

committing

a

crime

but

in

each

case

risk

analysis

could

have

been

used

to

avoid

or

to

help

mitigate

the

theft

It

is

becoming

a

necessity

that

companies

examine

their

business

practices

and

company

policies

to

avoid

risks

associated

with

information

stealing

The

solution

to

information

stealing

does

not

reside

in

technology

alone

but

also

requires

an

understanding

by

management

of

the

business

and

the

risks

associated

with

it

This

paper

examines

the

theft

of

information

from

different

companies

in

order

to

explain

the

short

coming

of

management

practices

that

lead

to

the

theft

In

May

of

Citigroup

lost

computer

tapes

that

were

being

sent

to

the

credit

bureau

via

UPS

that

included

Social

Security

numbers

and

payment

history

information

for

million

customers

After

this

event

this

New

York

based

company

has

decided

that

it

will

start

sending

its

data

to

the

credit

bureau

electronically

using

encryption

-LSB-

-RSB-

Citigroup

should

have

learned

a

lesson

from

Time

Warner

who

lost

a

shipment

of

backup

tapes

that

contained

personal

information

of

employees

that

was

being

sent

to

an

offsite

data

storage

company

in

March

of

-LSB-

-RSB-

But

the

question

remains

why

was

Citigroup

sending

sensitive

information

unsecured

Why

did

they

not

encrypt

the

data

in

the

first

place

and

why

did

they

realize

that

these

tapes

could

get

lost

or

stolen

as

evident

to

what

happened

with

Time

Warner

The

answer

is

because

they

did

not

correctly

identify

the

risk

Citigroup

believed

that

UPS

was

a

secure

method

for

sending

this

information

and

that

the

data

would

be

difficult

to

retrieve

off

the

tapes

because

of

the

hardware

needed

to

read

the

tapes

Citigroup

needed

to

evaluate

this

risk

of

properly

protecting

confidential

information

while

in

transmission

Now

Citigroup

has

the

issue

of

dealing

with

the

negative

public

associated

with

this

event

and

the

loss

of

any

potential

customers/revenue

it

will

lose

because

of

Permission

to

make

digital

or

hard

copies

of

all

or

part

of

this

work

for

personal

or

classroom

use

is

granted

without

fee

provided

that

copies

are

not

made

or

distributed

for

profit

or

commercial

advantage

and

that

copies

bear

this

notice

and

the

full

citation

on

the

first

page

To

copy

otherwise

or

republish

to

post

on

servers

or

to

redistribute

to

lists

requires

prior

specific

permission

and/or

a

fee

Information

Security

Curriculum

Development

-LRB-

InfoSecCD

-RRB-

Conference

September

Kennesaw

GA

USA

Copyright

ACM

it

This

issue

would

have

been

avoided

if

Citigroup

would

have

properly

identified

this

risk

and

taken

the

steps

to

protect

this

information

If

the

tapes

were

lost

and

the

data

was

encrypted

then

this

story

would

have

never

happened

Case

II

ChoicePoint

Choicepoint

has

made

more

than

acquisitions

since

to

make

it

one

of

the

largest

collections

of

personal

data

in

the

United

States

Choicepoint

sells

data

to

clients

doing

background

checks

on

job

and

loan

applicants

and

conducting

criminal

investigations

-LSB-

-RSB-

On

February

ChoicePoint

went

public

to

tell

people

that

identity

thieves

may

have

gained

access

to

their

personal

information

including

their

Social

Security

numbers

and

credit

reports

Authorities

believe

it

was

the

work

of

a

group

of

people

who

used

IDs

stolen

from

legitimate

business

people

to

set

up

phony

businesses

that

contracted

with

ChoicePoint

for

ID

checks

Bernknopf

-LRB-

ChoicePoint

's

spoke

person

-RRB-

said

-LSB-

-RSB-

With

ChoicePoint

's

security

incident

there

was

no

firewall

hacked

or

an

IDS

fooled

This

was

a

deceptive

scheme

that

took

advantage

of

security

holes

in

the

business

process

ChoicePoint

's

CISO

Rich

Baich

stated

The

mislabeling

of

this

event

as

a

hack

is

killing

ChoicePoint

It

's

such

a

negative

impression

that

suggests

we

failed

to

provide

adequate

protection

Fraud

happens

everyday

Hacks

do

n't

-LSB-

-RSB-

ChoicePoint

seemed

to

push

that

they

were

the

victims

of

fraud

and

not

at

fault

The

bottom

line

is

that

confidential

information

was

stolen

and

the

individuals

who

had

their

information

stolen

do

not

care

if

it

was

hacker

or

if

the

company

was

a

victim

of

fraud

ChoicePoint

failed

to

identify

holes

in

the

business

process

to

allow

this

event

to

happen

Which

if

someone

hacked

into

their

system

it

would

have

lead

to

the

same

result

the

theft

of

information

ChoicePoint

needs

to

recognize

that

identifying

risks

with

their

business

process

is

just

as

important

as

securing

their

information

system

from

an

external

hacker

Case

III

Egghead.com

Egghead

Software

was

a

company

that

opened

in

to

sell

computer

hardware

and

software

that

grew

to

have

more

than

stores

worldwide

Then

in

the

company

moved

its

business

to

the

internet

as

Egghead.com

In

December

of

Egghead.com

stated

that

a

hacker

has

breached

its

computer

system

and

may

have

gained

access

to

its

customer

database

-LSB-

-RSB-

Jerry

Kaplan

Egghead.com

's

co-chairman

stated

that

there

was

no

evidence

to

support

that

the

database

with

the

credit

card

numbers

for

its

customer

was

stolen

but

he

also

could

not

give

confirmation

that

they

were

not

stolen

Egghead

's

inability

to

determine

how

many

of

it

's

customers

credit

cards

had

been

compromised

may

mean

that

the

company

does

not

have

a

real-time

auditing

system

in

place

said

Paul

Robertson

senior

developer

for

security

service

firm

TruSecure

Corp.

If

you

do

n't

know

how

many

credit-card

numbers

you

lost

you

are

giving

a

quick

blanket

worst-case

answer

and

then

finding

out

what

happened

afterwards

he

said

-LSB-

-RSB-

The

way

that

Egghead.com

handled

its

security

incident

showed

that

they

did

not

have

a

good

plan

to

manage

the

theft

of

information

and

it

appeared

as

if

they

made

the

plan

to

handle

this

situation

as

it

happened

This

lack

of

planning

and

risk

analysis

by

management

caused

Egghead.com

's

business

to

suffer

tremendously

Shortly

thereafter

this

event

Egghead.com

went

into

bankruptcy

and

on

November

Amazon.com

acquired

Egghead.com

's

assets

in

the

Bankruptcy

Court

-LSB-

-RSB-

It

appears

the

inability

for

Egghead.com

to

successful

determine

with

certainty

the

extent

of

information

stolen

caused

more

damage

to

the

company

's

reputation

then

the

actual

event

itself

If

Egghead.com

had

a

well

developed

incident

response

plan

in

place

to

handle

this

security

breach

and

a

way

to

handle

the

media

that

followed

Egghead.com

may

have

been

able

to

weather

the

storm

and

stay

in

business

But

all

customer

confidence

was

lost

and

Egghead.com

was

not

able

to

recover

Case

IV

New

Jersey

Crime

Ring

Bank

employees

for

Wachovia

Corporation

Bank

of

America

Corporation

Commerce

Bancorp

Inc.

and

PNC

Bank

stole

information

on

customer

accounts

that

are

all

New

Jersey

residents

It

is

considered

the

largest

banking

security

breach

in

history

by

the

U.S.

Department

of

the

Treasury

The

suspects

pulled

up

the

account

data

while

working

inside

their

banks

then

printed

out

screen

captures

of

the

information

or

wrote

it

out

by

hand

Lomia

-LRB-

a

New

Jersey

Police

Detective

-RRB-

said

The

data

was

then

provided

to

a

company

called

DRL

Associates

Inc.

which

had

been

set

up

as

a

front

for

the

operation

DRL

advertised

itself

as

a

deadbeat-locator

service

and

as

a

collection

agency

but

was

not

properly

licensed

for

those

activities

by

the

state

police

said

-LSB-

-RSB-

With

this

security

breach

there

was

no

technology

involved

No

hackers

breached

the

information

system

This

was

completely

an

inside

job

The

question

becomes

of

how

this

could

have

been

prevented

The

answer

is

that

in

some

cases

the

theft

of

information

can

not

be

prevented

The

only

the

thing

that

management

can

do

is

be

prepared

for

when

it

does

happen

Because

of

incidents

like

this

it

is

becoming

a

duty

of

management

to

have

an

incident

response

plan

in

place

long

before

a

security

breach

happens

From

a

risk

analysis

viewpoint

an

incident

like

this

is

difficult

to

detect

and

almost

impossible

to

stop

before

it

happens

But

when

it

does

happen

and

the

criminals

are

caught

it

becomes

a

necessity

to

punish

the

ones

responsible

to

the

full

extent

of

the

law

to

deter

others

from

following

suit

Case

V

LexisNexis

LexisNexis

is

provider

of

legal

and

business

data

In

March

of

LexisNexis

announced

that

the

information

on

people

was

stolen

These

breaches

occurred

at

one

of

the

subsidiary

companies

Seisint

Inc.

Seisnt

Inc.

was

the

company

who

was

the

provider

of

data

to

the

Multistate

Anti-Terrorism

Information

Exchange

-LRB-

MATRIX

-RRB-

system

LexisNexis

which

acquired

Seisint

of

Boca

Raton

Florida

in

September

for

million

expressed

regret

over

the

incident

and

said

that

it

is

notifying

the

individuals

whose

information

may

have

been

accessed

and

will

provide

them

with

credit-monitoring

services

-LSB-

-RSB-

In

this

incident

hackers

stole

username

and

passwords

of

legitimate

users

to

access

the

confidential

information

In

a

statement

Kurt

Sanford

president

and

CEO

of

LexisNexis

Corporate

and

Federal

Markets

said

that

the

company

will

improve

the

user

ID

and

password

administration

procedures

that

its

customers

use

and

will

devote

more

resources

to

protecting

user

's

privacy

and

reinforcing

the

importance

of

privacy

-LSB-

-RSB-

This

security

breach

is

very

similar

to

the

incident

that

happened

at

ChoicePoint

who

is

one

of

LexisNexis

's

competitors

There

are

several

policies

that

should

have

been

implemented

that

could

have

reduced

the

risk

of

this

security

breach

Since

LexisNexis

gives

third

parties

access

to

its

confidential

information

there

becomes

a

need

to

educate

these

organizations

on

certain

practices

to

protect

the

data

Where

was

this

education

and

was

there

a

lack

of

education

due

to

the

possible

effect

that

it

could

have

on

business

Also

what

was

the

password

policy

for

its

customers

LexisNexis

has

not

elaborated

on

the

details

of

the

security

breach

but

considering

the

statement

of

the

CEO

of

LexisNexis

after

the

incident

there

clearly

seems

that

there

was

a

failure

to

detect

the

risk

associated

with

their

customer

's

password

policy

that

could

result

in

a

theft

of

information

LexisNexis

inability

to

properly

assess

this

risk

caused

the

security

breach

Through

education

and

a

secure

password

administration

policy

this

event

could

have

been

avoided

When

analyzing

these

case

studies

an

important

thing

to

ponder

is

that

for

every

security

breach

reported

how

many

go

unreported

These

security

breaches

could

have

been

avoided

with

proper

risk

assessment

and

risk

analysis

or

at

least

the

probability

of

a

security

breach

could

have

been

reduced

greatly

For

all

security

breaches

the

prevention

or

at

least

the

reduction

of

the

probability

of

the

security

breach

begins

and

ends

with

decisions

that

management

makes

In

an

organization

when

a

security

breach

occurs

it

causes

a

company

to

re-evaluate

their

policies

that

guide

their

information

security

With

this

rash

of

security

incidents

that

have

recently

taken

place

companies

do

not

need

to

wait

until

a

security

breach

happens

to

evaluate

their

security

policies

and

analyze

their

risks

Companies

need

to

have

an

ongoing

risk

analysis

that

is

continually

developed

and

re-developed

They

need

policies

that

are

ever

changing

to

meet

new

threats

and

new

security

weaknesses

from

a

both

business

practices

and

technology

viewpoints

Looking

at

the

incidents

that

happened

at

ChoicePoint

LexisNexis

and

Citigroup

these

companies

have

technological

solutions

to

protect

their

data

from

being

stolen

but

they

failed

at

weighing

equal

importance

the

security

of

the

data

from

a

business

issue

perspective

This

showed

in

their

inability

to

properly

evaluate

the

risk

in

the

business

practices

In

several

of

the

cases

the

theft

of

information

occurred

because

of

the

business

practices

of

the

company

and

technology

was

not

even

involved

Also

companies

need

to

learn

from

the

mistakes

of

others

because

history

will

repeat

itself

if

the

lesson

is

not

learned

There

is

an

age

old

saying

that

is

a

wise

person

learns

from

their

mistakes

but

an

even

wiser

person

learns

from

the

mistakes

of

others

Citigroup

needed

this

advice

With

Citigroup

's

loss

of

their

backup

tapes

they

should

have

learned

from

the

mistake

that

Time

Warner

made

just

months

earlier

but

they

did

not

Security

policies

and

practices

need

the

flexibility

to

change

and

management

has

a

responsibility

to

make

these

changes

when

new

threats

or

new

weakness

surface

so

that

they

can

protect

their

data

Companies

and

organizations

need

to

realize

the

importance

of

making

information

security

a

business

issue

as

well

as

a

technological

one

With

the

issue

that

happened

with

Egghead.com

they

did

have

security

systems

in

place

to

protect

their

data

from

being

stolen

but

it

lacked

the

kind

of

coordinated

organizational

response

necessary

to

convince

customers

and

shareholders

that

their

sensitive

data

were

actually

secure

Egghead.com

lost

of

its

stock

value

when

their

customer

data

was

stolen

-LSB-

-RSB-

Egghead.com

was

not

ready

for

the

media

storm

that

followed

the

security

breach

which

ultimately

caused

their

collapse

By

making

information

security

a

business

issue

as

well

as

a

technological

one

companies

can

add

strategic

operational

and

organizational

defenses

to

protect

their

data

As

more

identity

thefts

occur

companies

that

make

their

money

from

storing

this

information

are

going

to

become

liable

The

ChoicePoint

scandal

has

been

a

wake

up

call

for

how

vulnerable

consumers

are

to

identity

theft

because

of

the

lack

of

security

standards

for

the

largely

unregulated

information

broker

industry

said

Gail

Hillebrand

Senior

Attorney

for

Consumers

Union

's

West

Coast

Office

This

bill

will

ensure

that

information

brokers

are

held

accountable

for

enforcing

tough

security

practices

to

prevent

thieves

from

gaining

access

to

sensitive

consumer

data

And

it

gives

consumers

important

new

rights

to

examine

the

information

maintained

about

them

and

to

correct

any

errors

they

may

find

-LSB-

-RSB-

Companies

need

to

find

the

importance

of

protecting

their

data

from

both

technology

and

business

practices

weaknesses

Companies

view

the

protection

of

their

data

from

a

technology

issue

but

fail

to

realize

the

importance

that

management

plays

in

protecting

their

systems

with

the

creation

of

policies

and

understanding

the

risks

that

face

their

information

systems

From

a

consumer

standpoint

if

a

company

is

making

profit

from

someone

's

personal

information

and

they

fail

to

protect

this

data

should

they

not

give

some

sort

of

reputation

Companies

own

and

manage

consumer

information

and

individuals

have

little

power

over

their

information

that

is

controlled

by

these

organizations

As

identity

theft

continues

and

companies

fail

at

protecting

their

data

legislation

will

be

passed

that

will

force

companies

to

comply

with

regulator

standards

that

may

force

companies

to

give

this

reputation

to

individuals

who

have

their

identity

stolen

Today

there

are

only

laws

to

protect

data

in

certain

industries

This

includes

the

Health

Insurance

Portability

and

Accountability

Act

for

healthcare

and

the

Gramm-Leach-Bliley

Act

for

financial

services

With

consumer

groups

voicing

their

opinions

regarding

the

theft

of

information

from

companies

the

US

Congress

and

other

state

legislators

are

getting

prepared

to

pass

broader

data

privacy

protection

to

protect

consumers

-LSB-

-RSB-

There

are

steps

that

companies

and

organizations

need

to

take

to

protect

themselves

from

the

theft

of

information

First

companies

need

to

be

prepared

when

a

security

breach

occurs

because

a

risk

to

an

asset

is

never

zero

percent

Organizations

need

to

establish

policies

and

risk

assessments

that

protect

their

data

from

both

technology

risks

and

business

practices

well

before

a

security

breach

occurs

This

is

achieved

by

companies

having

the

organizational

structure

that

allows

management

to

fully

understand

the

business

processes

and

technology

that

expose

their

information

systems

to

threats

Also

companies

need

the

ability

to

change

and

adapt

to

new

threats

that

oppose

their

information

It

is

not

possible

to

prevent

all

security

breaches

that

lead

to

a

theft

of

information

but

companies

will

need

to

have

policies

and

practices

in

place

to

better

protect

the

data

Companies

will

need

not

only

to

weigh

technology

risk

to

their

information

but

also

understand

business

issues

that

expose

their

information

to

theft

It

no

longer

matters

how

the

information

stolen

whether

it

was

a

hacker

or

a

social

engineer

that

committed

the

crime

companies

need

to

protect

their

information

from

all

threats

and

minimize

their

risks

from

all

aspects

-LSB-

-RSB-

Charny

Ben

and

Lemos

Robert

December

Egghead

Scrambles

to

Guage

Damage

Retrieved

from

-LSB-

-RSB-

Crawford

Michael

June

Criminals

Grasp

the

Metrics

of

Information

Value

Retrieved

from

eid

-LSB-

-RSB-

ConsumersUnion.org

Consumers

Union

applauds

Nelson

-LRB-

FL

-RRB-

bill

to

extend

federal

oversight

to

information

brokers

like

ChoicePoint

Retrieved

from

-LSB-

-RSB-

Easen

Nick

April

Cyber

Crime

is

Right

Under

Your

Nose

Retrieved

from

ty/index.html

-LSB-

-RSB-

Gross

Grant

February

ChoicePoint

's

Error

Sparks

Talk

of

ID

Theft

Law

Retrieved

from

-LSB-

-RSB-

Liu

Bob

December

Eggheacd.com

Becomes

Amazon.com

Property

Retrieved

from

-LSB-

-RSB-

McKinsey

Company

Inc.

June

Managing

Information

Security

Retrieved

from

-LSB-

-RSB-

McMillian

Robert

June

Citigroup

to

Encrypt

Data

Sent

to

Credit

Bureaus

Retrieved

from

html

-LSB-

-RSB-

Mearian

Lucas

May

Time

Warner

Says

Data

of

Workers

Lost

Retrieved

from

html

-LSB-

-RSB-

Mimoso

Michael

April

Damage

Control

Retrieved

from

html

-LSB-

-RSB-

Rasmussen

Michael

March

ChoicePoint

Security

Breach

Will

Lead

to

Increased

Regulation

Retrieved

from

-LSB-

-RSB-

Robert

Paul

March

Hackers

Grab

LexisNexis

Info

on

People

Retrieved

from

RSS

RSS

asp

-LSB-

-RSB-

Weiss

Todd

May

Scope

of

Bank

Data

Theft

Grows

to

Customers

Retrieved

from

html

O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O B I O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

risk analysis
personal information
cyber crime
encryption
incident response plan
privacy
management issue
information system
confidential information
security breach
information security
theft of information

data protection
information security management
security management
human factor
data protection procedure
business practises and policy
cases of information theft