Data Mining, Security and Privacy

1. Data Mining, Security and Privacy Prof. Bhavani Thuraisingham Prof. Murat Kantarcioglu Ms Li Liu (PhD Student – completing December 2007) The University of Texas at Dallas August 24, 2008

2. Outline • Data Mining for Security Applications • Privacy Concerns • What is Privacy? • Why is data mining a threat to privacy • Developments in Privacy • Directions for Privacy • Confidentiality, Privacy and Trust for Data Mining

3. Data Mining Needs for Counterterrorism: Non-real-time Data Mining • 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, . . . • Integrate the data, build warehouses and federations • Develop profiles of terrorists, activities/threats • Mine the data to extract patterns of potential terrorists and predict future activities and targets • Find the “needle in the haystack” - suspicious needles? • Data integrity is important • Techniques have to SCALE

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

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

6. What should be done: Form a Research Agenda • Immediate action (0 - 1 year) • We’ve got to know what our current capabilities are • Do the commercial tools scale? Do they work only on special data and limited cases? Do they deliver what they promise? • Need an unbiased objective study with demonstrations • At the same time, work on the big picture • What do we want? What are our end results for the foreseeable future? What are the criteria for success? How do we evaluate the data mining algorithms? What testbeds do we build? • Near-term (1 - 3 years) • Leverage current efforts • Fill the gaps in a goal-directed way; technology transfer • Long-term (3 - 5 years and beyond) • 5-year R&D plan for data mining for counterterrorism

7. IN SUMMARY: • 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) • e.g., Neural networks to detect abnormal patterns • Tools are being examined to determine abnormal patterns for national security • Classification techniques, Link analysis • Fraud detection • Credit cards, calling cards, identity theft etc. BUT CONCERNS FOR PRIVACY

8. What is Privacy • Medical Community • Privacy is about a patient determining what information the doctor should release about him/her • Financial community • A bank customer determines what financial information the bank should release about him/her • Government community • FBI would collect information about US citizens. However FBI determines what information about a US citizen it can release to say the CIA

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

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

11. Some Privacy Problems and Potential Solutions • Problem: Privacy violations that result due to data mining • Potential solution: Privacy-preserving data mining • 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 • Problem: Privacy violations due to un-encrypted data • Potential solution: Encryption at different levels • Problem: Privacy violation due to poor system design • Potential solution: Develop methodology for designing privacy-enhanced systems

12. Privacy as Inference:Privacy Constraint Processing • Privacy constraint/policy 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 • Augment a database system with a privacy controller for constraint processing

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

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

15. Privacy Preserving Data Mining • 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 • 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 • Secure Multi-party Computation • Each party knows its own inputs; encryption techniques used to compute final results • Rules, predictive functions • Approach: Only make a sample of data available • Limits ability to learn good classifier

16. Cryptographic Approaches for Privacy Preserving Data Mining • Secure Multi-part Computation (SMC) for PPDM • Mainly used for distributed data mining.; Provably secure under some assumptions.; Learned models are accurate; Mainly semi-honest assumption (i.e. parties follow the protocols); Malicious model is also explored recently. (e.g. Kantarcioglu and Kardes paper in this workshop); Many SMC based PPDM algorithms share common sub-protocols (e.g. dot product, summation, etc. ) • Drawbacks: • Still not efficient enough for very large datasets. (e.g. petabyte sized datasets ??); Semi-honest model may not be realistic;Malicious model is even slower • Possible new directions • New models that can trade-off better between efficiency and security; Game theoretic / incentive issues in PPDM; Combining anonymization and cryptographic techniques for PPDM

17. Perturbation Based Approaches for Privacy Preserving Data Mining • Goal: Distort data while still preserve some properties for data mining propose. • Goal: Achieve a high data mining accuracy with maximum privacy protection. • Additive Based • Multiplicative Based • Condensation based • Decomposition • Data Swapping • Our approach: Privacy is a personal choice, so should be individually adaptable (Liu, Kantarcioglu and Thuraisingham ICDM’06)

18. Perturbation Based Approaches for Privacy Preserving Data Mining • The trend is to make PPDM approaches to reflect reality • We investigated perturbation based approaches with real-world data sets • We give a applicability study to the current approaches • Liu, Kantarcioglu and Thuraisingham, DKE 07 • We found out, • The reconstruction of the original distribution may not work well with real-world data sets • Try to modify perturbation techniques, and adapt some data mining tools, e.g. Liu, Kantarcioglu and Thuraisingham, Novel decision tree – UTD technical report 06

19. Platform for Privacy Preferences (P3P): What is it? • P3P is an emerging industry standard that enables web sites t9o express their privacy practices in a standard format • The format of the policies can be automatically retrieved and understood by user agents • It is a product of W3C; World wide web consortium www.w3c.org • When a user enters a web site, the privacy policies of the web site is conveyed to the user; If the privacy policies are different from user preferences, the user is notified; User can then decide how to proceed • Several major corporations are working on P3P standards including

20. Privacy for Assured Information Sharing Data/Policy for Federation Export Export Data/Policy Data/Policy Export Data/Policy Component Component Data/Policy for Data/Policy for Agency A Agency C Component Data/Policy for Agency B

21. Privacy Preserving Surveillance Raw video surveillance data Face Detection and Face Derecognizing system Suspicious people found Faces of trusted people derecognized to preserve privacy Suspicious events found Comprehensive security report listing suspicious events and people detected Suspicious Event Detection System Manual Inspection of video data Report of security personnel

22. Directions: Foundations of Privacy Preserving Data Mining • We proved in 1990 that the inference problem in general was unsolvable, therefore the suggestion was to explore the solvability aspects of the problem. • Can we do something similar for privacy? • Is the general privacy problem solvable? • What are the complicity classes? • What is the storage and time complicity • We need to explore the foundation of PPDM and related privacy solutions

23. Directions: Testbed Development and Application Scenarios • There are numerous PPDM related algorithms. How do they compare with each other? We need a testbed with realistic parameters to test the algorithms • It is time to develop real world scenarios where these algorithms can be utilized • Is it feasible to develop realistic commercial products or should each organization adapt product to suit their needs?

24. Key Points • 1. There is no universal definition for privacy, each organization must definite what it means by privacy and develop appropriate privacy policies • 2. Technology alone is not sufficient for privacy We need technologists, Policy expert, Legal experts and Social scientists to work on Privacy • 3. Some well known people have said ‘Forget about privacy” Therefore, should we pursue research on Privacy? • Interesting research problems, there need to continue with research • Something is better than nothing • Try to prevent privacy violations and if violations occur then prosecute • 4. We need to tackle privacy from all directions

25. Application Specific Privacy? • Examining privacy may make sense for healthcare and financial applications • Does privacy work for Defense and Intelligence applications? • Is it even meaningful to have privacy for surveillance and geospatial applications • Once the image of my house is on Google Earth, then how much privacy can I have? • I may want my location to be private, but does it make sense if a camera can capture a picture of me? • If there are sensors all over the place, is it meaningful to have privacy preserving surveillance? • This suggests that we need application specific privacy • It is not meaningful to examine PPDM for every data mining algorithm and for every application

26. CPT: Confidentiality, Privacy and Trust • I as a user of Organization A send data about me to organization B, I read the privacy policies enforced by organization B • If I agree to the privacy policies of organization B, then I will send data about me to organization B • If I do not agree with the policies of organization B, then I can negotiate with organization B • Even if the web site states that it will not share private information with others, do I trust the web site • Note: while confidentiality is enforced by the organization, privacy is determined by the user. Therefore for confidentiality, the organization will determine whether a user can have the data. If so, then the organization van further determine whether the user can be trusted

27. Confidentiality, Privacy and Trust • How can we ensure the confidentiality of the data mining processes and results? • Access control policies • How can we trust the data mining processes and results • Verification and validation • How can we integrate confidentiality, privacy and trust with respect to data mining? • Need to examine the research challenges and form a research agenda

28. Data Mining and Privacy: Friends or Foes? • They are neither friends nor foes • Need advances in both data mining and privacy • 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 • Technologists, legal specialists, social scientists, policy makers and privacy advocates MUST work together