Lecture21

Data and Applications Security Developments and Directions

Dr. Bhavani ThuraisinghamThe University of Texas at Dallas

Lecture #21Privacy

March 29, 2005

Outline

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

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

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

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

Some Directions: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

Some Directions: Privacy Problem as a form of Inference Problem

Privacy constraints- Content-based constraints; association-based constraints

Privacy controller- Augment a database system with a privacy controller for

constraint processing and examine the releasability of data/information (e.g., release constraints)

Use of conceptual structures to design applications with privacy in mind (e.g., privacy preserving database and application design)

The web makes the problem much more challenging than the inference problem we examined in the 1990s!

Is the General Privacy Problem Unsolvable?

Privacy Constraint Processing

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

Augment a database system with a privacy controller for constraint processing

Architecture for Privacy Constraint Processing

User Interface Manager

ConstraintManager

Privacy Constraints

Query Processor:

Constraints during query and release operations

Update Processor:

Constraints during update operation

Database Design Tool

Constraints during database design operation

DatabaseDBMS

Semantic Model for Privacy Control

Patient John

CancerInfluenza

Has disease

Travels frequently

England

address

John’s address

Dark lines/boxes containprivate information

Some Directions:Encryption for Privacy

Encryption at various levels- Encrypting the data as well as the results of data mining- Encryption for multi-party computation

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

Jointly develop privacy policies with policy specialists Specification language for privacy policies Generate privacy constraints from the policy and check for

consistency of constraints Develop a privacy model Privacy architecture that identifies privacy critical components Design and develop privacy enforcement algorithms Verification and validation

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

Aspects of Privacy

Privacy Preserving Databases- Privacy Constraint Processing

Privacy Preserving Networks- Sensor networks, - - - -

Privacy Preserving Surveillance- RFID

Privacy Preserving Semantic Web- XML, RDF, - - - -

Privacy Preserving Data Mining

Revisiting Privacy Preserving Data Mining

Association Rules- Privacy Preserving Association Rule Mining

IBM, - - - - - Decision Trees- Privacy Preserving Decision Trees

IBM, - - - - Clustering- Privacy Preserving Clustering

Purdue, - - - - Link Analysis- Privacy Preserving Link Analysis

UTD, - - - - -

Privacy Preserving Data MiningAgrawal and Srikant (IBM)

Value Distortion- Introduce a value Xi + r instead of Xi where r is a

random value drawn from some distributionUniform, Gaussian

Quantifying privacy- Introduce a measure based on how closely the

original values of modified attribute can be estimated

Challenge is to develop appropriate models- Develop training set based on perturbed data

Evolved from inference problem in statistical databases

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 consortiumwww.w3c.org

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

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

Platform for Privacy Preferences (P3P): Organizations

Several major corporations are working on P3P standards including:-Microsoft- IBM- HP- NEC- Nokia- NCR

Web sites have also implemented P3PSemantic web group has adopted P3P

Platform for Privacy Preferences (P3P): Specifications

Initial version of P3P used RDF to specify policiesRecent version has migrated to XMLP3P Policies use XML with namespaces for

encoding policiesExample: Catalog shopping- Your name will not be given to a third party but

your purchases will be given to a third party- <POLICIES xmlns =

http://www.w3.org/2002/01/P3Pv1><POLICY name = - - - -</POLICY></POLICIES>

Platform for Privacy Preferences (P3P): Specifications (Concluded)

P3P has its own statements a d data types expressed in XML

P3P schemas utilize XML schemasXML is a prerequisite to understanding P3PP3P specification released in January 20005 uses

catalog shopping example to explain conceptsP3P is an International standard and is an ongoing

project

P3P and Legal Issues

P3P does not replace lawsP3P work together with the lawWhat happens if the web sites do no honor their

P3P policies- Then appropriate legal actions will have to be

takenXML is the technology to specify P3P policiesPolicy experts will have to specify the policiesTechnologies will have to develop the

specificationsLegal experts will have to take actions if the

policies are violated

Challenges and Discussion

Technology alone is not sufficient for privacyWe need technologists, Policy expert, Legal

experts and Social scientists to work on PrivacySome well known people have said ‘Forget about

privacy”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

Discussion?