1. Prof. Bhavani Thuraisingham The University of Texas at Dallas
Privacy
Prof. Bhavani Thuraisingham
The University of Texas at Dallas
July 2011

2. What is Privacy Medical Community
Privacy is about a patient determining what patient/medical information the doctor should be released about him/her
Financial community
A bank customer determine 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

3. 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

4. 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

5. 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

6. 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

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

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

9. 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
Interest measures – make sure that sensitive facts, if they exist, will be deemed uninteresting by algorithms
Extra data – example, a “phone book” that contains extra entries. Still useful if goal is to find phone given name, but access to complete phone book doesn’t allow determining facts about (for example) department sizes.
Performance – maybe not an issue for small amounts of data, but on large data sets (terabyte); exponential performance is an issue (disk limited)
Note that we don’t have the same problem faced by (for example) the GPS military/civilian accuracy encoding. There, the goal is to make information (position) known to all, but just more clearly for some. Here, the information to be made known, and the information to be kept hidden, are completely different. A better analogy would be getting position from communications satellites (e.g. measuring delay). Introducing a small random delay will wreak havoc with trying to determine position by this method, but will not alter the information communicated.

10. 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
Efficient/specific cryptographic solutions for many distributed data mining problems are developed.
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. )
Here I try to summarize entire cryptographic approaches to PPDM. Basically, lots of work done on applying secure
Multi-party based ideas for PPDM. Generally, it is applied to distributed data mining. In most of the work recent work, it is assumed
That the adversaries are semi-honest (i.e. they follow the protocol correctly). Only recently (including Kantarcioglu and Kardes paper that will be presented in the workshop) malicious model is discussed. It turns out that all these
Different solutions are consist of few common secure subprotocols such as dot product and summation.

11. Cryptographic Approaches for Privacy Preserving Data Mining
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
Main drawbacks for the SMC approaches for PPDM are:
1) They are not efficient enough for really large data sets and high number of distributed nodes.
2) Semi-honest assumption may not be realistic
3) Malicious model is even slower.
Possible future directions :
New models.
Crypto methods assume either malicious or semi-honest adversaries. What about rational adversaries (i.e. Adversaries that cheat for profit)? Game theoretic models can provide efficient solutions.
We may try combining anonymization with SMC techniques for efficient and accurate solutions.

12. Perturbation Based Approaches for Privacy Preserving Data Mining
Goal: Distort data while still preserve some properties for data mining propose.
Additive Based
Multiplicative Based
Condensation based
Decomposition
Data Swapping
Perturbation is a very important technique in PPDM. This technique is to distort the data, but still keep some properties of the data which will be used for later data mining phase.
Here listed are some perturbation techniques.
Additive based approach is first proposed by Agrawal and Srikant, now has many various. Single one step plus may not enough to protect the privacy, we have proposed a two step model in ICDM 06.
Multiplicative based approach, e.g. orthogonal transformation, geometry property is to rotate the data. E.g Chen and Liu ICDM 05. This transformation has the property to keep the Euclidean distance between any pair of data points, so some data mining tools can be directly applied, K-Nearest Neighbor Classifier(KNN), Support Vector Machines(SVM) and so on. The later approach has evaluated the privacy preserving in more detail, so proposed a random projection to a lower space, Liu and Kargupta TKDE2006 and Liu and Kargupta PKDD'06.
Condensation and decomposition (Wandand Zhang ICDM06) are using some properties of matrix. In the decomposition area, Wavelet transformation is new. All these approaches are still in progress.
Data swapping is a different approach, which transforms the data set by switching a subset of attributes between selected pairs (Fienberg et al 2003.

13. Perturbation Based Approaches for Privacy Preserving Data Mining
Goal: Achieve a high data mining accuracy with maximum privacy protection.
Still a very challenge area.
From the figure we can see, to achieve 100% privacy is easy, simply don’t give out any information. No credit card, no marketing. To ignore the privacy to conduct data mining is easy too. What is privacy? Who cares.
Our goal is tough, and will probably end up in that square for the acceptable results.

14. Perturbation Based Approaches for Privacy Preserving Data Mining
Privacy is a personal choice, so should enable individual adaptable (Liu, Kantarcioglu and Thuraisingham ICDM’06)
We have proposed an individual adaptable model, which enable individuals to choose their own privacy level. User is to choose their privacy level, the system will match with different interval length use to perturb the data in our two phase perturbation model. Our two phase model is additive based approach, but we introduce the second step sampling to enhance the privacy preserving.

15. Perturbation Based Approaches for Privacy Preserving Data Mining
The trend is to make PPDM approaches fit in the 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 the original distribution may not work well with real-world data set
Distribution is a hard problem, should not use as a media step
Try to modify perturbation techniques, and adapt some data mining tools, e.g. Liu, Kantarcioglu and Thuraisingham, Novel decision tree – UTD technical report 06
Make the PPDM approaches more fit in the real life situation is the trend for today’s research. We conducted intensive experiments with real-world data set, and give a applicability study in DKE07 paper.
Reconstruction of the original data distribution not work very well with real life data. Distribution is a hard problem. When the distribution of the original data set is not hard, the method may work; but if the distribution of the original data is hard, the method not work well. It depends on the distribution!
So we suggest should not use distribution as a meddle step. In our another work, we have tailed the data mining tools to fit the PPDM domain. That is try to directly mapping the data mining functions according to the noise addition method. Believe this is a fruitful direction for PPDM

16. CPT: Confidentiality, Privacy and Trust
Before 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

17. Platform for Privacy Preferences (P3P): What is it?
P3P is an emerging industry standard that enables web sites to 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
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

18. 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 P3P
Semantic web group has adopted P3P

19. Platform for Privacy Preferences (P3P): Specifications
Initial version of P3P used RDF to specify policies; Recent version has migrated to XML
P3P Policies use XML with namespaces for encoding policies
P3P has its own statements and data types expressed in XML; P3P schemas utilize XML schemas
P3P specification released in January uses catalog shopping example to explain concepts; P3P is an International standard and is an ongoing project
Example: Catalog shopping
Your name will not be given to a third party but your purchases will be given to a third party
<POLICIES xmlns =
<POLICY name =
</POLICY>
</POLICIES>

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

21. 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

22. 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

23. 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

24. 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?

25. 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

26. 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 suggestion that we need application specific privacy
It is not meaningful to examine PPDM for every data mining algorithm and for every application

27. 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