Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #21 Privacy March 29, 2005

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

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

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

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

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

Some Directions: Privacy Problem as a form of Inference Problem l Privacy constraints - Content-based constraints; association-based constraints l Privacy controller - Augment a database system with a privacy controller for constraint processing and examine the releasability of data/information (e.g., release constraints) l Use of conceptual structures to design applications with privacy in mind (e.g., privacy preserving database and application design) l The web makes the problem much more challenging than the inference problem we examined in the 1990s! l Is the General Privacy Problem Unsolvable?

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

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

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

Some Directions: Encryption for Privacy l Encryption at various levels - Encrypting the data as well as the results of data mining - Encryption for multi-party computation l Encryption for untrusted third party publishing - Owner enforces privacy policies - Publisher gives the user only those portions of the document he/she is authorized to access - Combination of digital signatures and Merkle hash to ensure privacy

Some Directions: Methodology for Designing Privacy Systems l Jointly develop privacy policies with policy specialists l Specification language for privacy policies l Generate privacy constraints from the policy and check for consistency of constraints l Develop a privacy model l Privacy architecture that identifies privacy critical components l Design and develop privacy enforcement algorithms l Verification and validation

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

Aspects of Privacy l Privacy Preserving Databases - Privacy Constraint Processing l Privacy Preserving Networks - Sensor networks, l Privacy Preserving Surveillance - RFID l Privacy Preserving Semantic Web - XML, RDF, l Privacy Preserving Data Mining

Revisiting Privacy Preserving Data Mining l Association Rules - Privacy Preserving Association Rule Mining l IBM, l Decision Trees - Privacy Preserving Decision Trees l IBM, l Clustering - Privacy Preserving Clustering l Purdue, l Link Analysis - Privacy Preserving Link Analysis l UTD,

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

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 l Main difference between privacy and security - User is informed of the privacy policies - User is not informed of the security policies

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

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

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 - <POLICY name =

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 uses catalog shopping example to explain concepts l P3P is an International standard and is an ongoing project

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

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?