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

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

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

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

Data Mining for Real-time Threats

Integratedatasources in real-time

Buildreal-timemodels

ExamineResults in Real-time

Reportfinalresults

Data sourceswith informationabout terroristsand terrorist activities

Minethedata

Rapidlysift throughdata and discardirrelevant data

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

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

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

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

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

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

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

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

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)

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

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

Privacy for Assured Information Sharing

ExportData/Policy

ComponentData/Policy for

Agency A

Data/Policy for Federation

ExportData/Policy

ComponentData/Policy for

Agency C

ComponentData/Policy for

Agency B

ExportData/Policy

Privacy Preserving Surveillance

Raw video surveillance data

Face Detection and Face Derecognizing system

Suspicious Event Detection System

Manual Inspection of video data

Comprehensive security report listing suspicious events and people detected

Suspicious people found

Suspicious events found

Report of security personnel

Faces of trusted people derecognized to preserve privacy

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

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?

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

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

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

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

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