1. Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture ##9 Data Mining, Security and Privacy March 21, 2007

2. Objective of the Unit l This unit provides an overview of data mining for security (national security) and then discusses privacy

3. Data Mining for Counter-terrorism

4. Data Mining Needs for Counterterrorism: Non-real-time Data Mining l Gather data from multiple sources - Information on terrorist attacks: who, what, where, when, how - Personal and business data: place of birth, ethnic origin, religion, education, work history, finances, criminal record, relatives, friends and associates, travel history,... - Unstructured data: newspaper articles, video clips, speeches, emails, phone records,... l Integrate the data, build warehouses and federations l Develop profiles of terrorists, activities/threats l Mine the data to extract patterns of potential terrorists and predict future activities and targets l Find the “needle in the haystack” - suspicious needles? l Data integrity is important l Techniques have to SCALE

5. Data Mining for Non Real-time Threats Integrate data sources Clean/ modify data sources Build Profiles of Terrorists and Activities Examine results/ Prune results Report final results Data sources with information about terrorists and terrorist activities Mine the data

6. Data Mining Needs for Counterterrorism: Real-time Data Mining l Nature of data - Data arriving from sensors and other devices l Continuous data streams - Breaking news, video releases, satellite images - Some critical data may also reside in caches l Rapidly sift through the data and discard unwanted data for later use and analysis (non-real-time data mining) l Data mining techniques need to meet timing constraints l Quality of service (QoS) tradeoffs among timeliness, precision and accuracy l Presentation of results, visualization, real-time alerts and triggers

7. Data Mining for Real-time Threats Integrate data sources in real-time Build real-time models Examine Results in Real-time Report final results Data sources with information about terrorists and terrorist activities Mine the data Rapidly sift through data and discard irrelevant data

8. Data Mining Outcomes and Techniques for Counter-terrorism

9. Example Success Story - COPLINK l COPLINK developed at University of Arizona - Research transferred to an operational system currently in use by Law Enforcement Agencies l What does COPLINK do? - Provides integrated system for law enforcement; integrating law enforcement databases - If a crime occurs in one state, this information is linked to similar cases in other states - It has been stated that the sniper shooting case may have been solved earlier if COPLINK had been operational at that time

10. Where are we now? l We have some tools for - building data warehouses from structured data - integrating structured heterogeneous databases - mining structured data - forming some links and associations - information retrieval tools - image processing and analysis - pattern recognition - video information processing - visualizing data - managing metadata

11. What are our challenges? l Do the tools scale for large heterogeneous databases and petabyte sized databases? l Building models in real-time; need training data l Extracting metadata from unstructured data l Mining unstructured data l Extracting useful patterns from knowledge-directed data mining l Rapidly forming links and associations; get the big picture for real- time data mining l Detecting/preventing cyber attacks l Mining the web l Evaluating data mining algorithms l Conducting risks analysis / economic impact l Building testbeds

12. IN SUMMARY: l Data Mining is very useful to solve Security Problems - Data mining tools could be used to examine audit data and flag abnormal behavior - Much recent work in Intrusion detection (unit #18) l e.g., Neural networks to detect abnormal patterns - Tools are being examined to determine abnormal patterns for national security l Classification techniques, Link analysis - Fraud detection l Credit cards, calling cards, identity theft etc. BUT CONCERNS FOR PRIVACY

13. Outline l Data Mining and Privacy - Review l Some Aspects of Privacy l Privacy Preserving Data Mining l Platform for Privacy Preferences l Challenges and Discussion

14. Some Privacy concerns l Medical and Healthcare - Employers, marketers, or others knowing of private medical concerns l Security - Allowing access to individual’s travel and spending data - Allowing access to web surfing behavior l Marketing, Sales, and Finance - Allowing access to individual’s purchases

15. Data Mining as a Threat to Privacy l Data mining gives us “facts” that are not obvious to human analysts of the data l Can general trends across individuals be determined without revealing information about individuals? l Possible threats: - Combine collections of data and infer information that is private l Disease information from prescription data l Military Action from Pizza delivery to pentagon l Need to protect the associations and correlations between the data that are sensitive or private

16. Some Privacy Problems and Potential Solutions l Problem: Privacy violations that result due to data mining - Potential solution: Privacy-preserving data mining l Problem: Privacy violations that result due to the Inference problem - Inference is the process of deducing sensitive information from the legitimate responses received to user queries - Potential solution: Privacy Constraint Processing l Problem: Privacy violations due to un-encrypted data - Potential solution: Encryption at different levels l Problem: Privacy violation due to poor system design - Potential solution: Develop methodology for designing privacy- enhanced systems

17. Some Directions: Privacy Preserving Data Mining l Prevent useful results from mining - Introduce “cover stories” to give “false” results - Only make a sample of data available so that an adversary is unable to come up with useful rules and predictive functions l Randomization - Introduce random values into the data and/or results - Challenge is to introduce random values without significantly affecting the data mining results - Give range of values for results instead of exact values l Secure Multi-party Computation - Each party knows its own inputs; encryption techniques used to compute final results - Rules, predictive functions l Approach: Only make a sample of data available - Limits ability to learn good classifier

18. Privacy Preserving Data Mining Agrawal and Srikant (IBM) l Value Distortion - Introduce a value Xi + r instead of Xi where r is a random value drawn from some distribution l Uniform, Gaussian l Quantifying privacy - Introduce a measure based on how closely the original values of modified attribute can be estimated l Challenge is to develop appropriate models - Develop training set based on perturbed data l Evolved from inference problem in statistical databases

19. Privacy Constraint Processing l Privacy constraints processing - Based on prior research in security constraint processing - Simple Constraint: an attribute of a document is private - Content-based constraint: If document contains information about X, then it is private - Association-based Constraint: Two or more documents taken together is private; individually each document is public - Release constraint: After X is released Y becomes private l Augment a database system with a privacy controller for constraint processing

20. Architecture for Privacy Constraint Processing User Interface Manager Constraint Manager Privacy Constraints Query Processor: Constraints during query and release operations Update Processor: Constraints during update operation Database Design Tool Constraints during database design operation Database DBMS

21. Semantic Model for Privacy Control Patient John Cancer Influenza Has disease Travels frequently England address John’s address Dark lines/boxes contain private information

22. Data Mining and Privacy: Friends or Foes? l They are neither friends nor foes l Need advances in both data mining and privacy l Need to design flexible systems - For some applications one may have to focus entirely on “pure” data mining while for some others there may be a need for “privacy-preserving” data mining - Need flexible data mining techniques that can adapt to the changing environments l Technologists, legal specialists, social scientists, policy makers and privacy advocates MUST work together

23. Platform for Privacy Preferences (P3P): What is it? l P3P is an emerging industry standard that enables web sites t9o express their privacy practices in a standard format l The format of the policies can be automatically retrieved and understood by user agents l It is a product of W3C; World wide web consortium www.w3c.org l Main difference between privacy and security - User is informed of the privacy policies - User is not informed of the security policies

24. Platform for Privacy Preferences (P3P): Key Points l When a user enters a web site, the privacy policies of the web site is conveyed to the user l If the privacy policies are different from user preferences, the user is notified l User can then decide how to proceed

25. Platform for Privacy Preferences (P3P): Organizations l Several major corporations are working on P3P standards including: - Microsoft - IBM - HP - NEC - Nokia - NCR l Web sites have also implemented P3P l Semantic web group has adopted P3P

26. Platform for Privacy Preferences (P3P): Specifications l Initial version of P3P used RDF to specify policies l Recent version has migrated to XML l P3P Policies use XML with namespaces for encoding policies l Example: Catalog shopping - Your name will not be given to a third party but your purchases will be given to a third party - http://www.w3.org/2002/01/P3Pv1 <POLICY name = - - - -

27. Platform for Privacy Preferences (P3P): Specifications (Concluded) l P3P has its own statements a d data types expressed in XML l P3P schemas utilize XML schemas l XML is a prerequisite to understanding P3P l P3P specification released in January 20005 uses catalog shopping example to explain concepts l P3P is an International standard and is an ongoing project

28. P3P and Legal Issues l P3P does not replace laws l P3P work together with the law l What happens if the web sites do no honor their P3P policies - Then appropriate legal actions will have to be taken l XML is the technology to specify P3P policies l Policy experts will have to specify the policies l Technologies will have to develop the specifications l Legal experts will have to take actions if the policies are violated

29. Challenges and Discussion l Technology alone is not sufficient for privacy l We need technologists, Policy expert, Legal experts and Social scientists to work on Privacy l Some well known people have said ‘Forget about privacy” l Should we pursue working on Privacy? - Interesting research problems - Interdisciplinary research - Something is better than nothing - Try to prevent privacy violations - If violations occur then prosecute l Discussion?