1. Background Knowledge Attack for Generalization based Privacy- Preserving Data Mining

2. Discussion Outline (sigmod08-4) Privacy-MaxEnt: Integrating Background Knowledge in Privacy Quantification (kdd08-4) Composition Attacks and Auxiliary Information in Data Privacy (vldb07-4) Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge

3. Anonymization techniques Generalization & suppression  Consistency property: multiple occurrences of the same value are always generalized the same way. (all old methods and recent Incognito)  No consistency property (Mondrain) Anatomy (Tao vldb06) Permutation (Koudas ICDE07)

4. Anonymization through Anatomy Anatomy: simple and effective privacy preservation

5. Anonymization through permutation

6. Background knowledge K-anonymity  Attacker has access to public databases, i.e., quasi- identifier values of the individuals.  The target individual is in the released database. L-diversity  Homogeneity attack  background knowledge about some individuals’ sensitive attribute values T-closeness  The distribution of sensitive attribute in the overall table

7. Type of background knowledge Known facts  A male patient cannot have ovarian cancer Demographical information  It is unlikely that a young patient of certain ethnic groups has heart disease  Some combination of the quasi-identifier values cannot entail some sensitive attribute values

8. Type of background knowledge Adversary-specific knowledge  target individual has no specific sensitive attribute value, e.g., Bob does not have flu  Sensitive attribute values of some other individuals, Joe, John, and Mike (as Bob’s neighbor) have flu  Knowledge about same-value family

9. Some extension Multiple sensitive values per individual  Flu \in Bob[S]  Basic implication (adopted in Martin ICDE07) cannot practically express the above --- |s|-1 basic implications are needed Probabilistic knowledge vs. deterministic knowledge

10. Data Sets IdentifierQuasi-Identifier (QI)Sensitive Attribute (SA) how much adversaries can know about an individual’s sensitive attributes if they know the individual’s quasi-identifiers

11. we need to measure P(SA|QI) Quasi-Identifier (QI)Sensitive Attribute (SA) Background Knowledge

12. Impact of Background Knowledge Background Knowledge: It ’ s rare for male to have breast cancer.

13. [Martin, et al. ICDE’07] first formal study of the effect of background knowledge on privacy-preserving

14. Assumption  the attacker has complete information about individuals ’ non-sensitive data Full identification information NameAgeSexZipcodeDisease Andy4M12000gastric ulcer Bill5M14000dyspepsia Ken6M18000pneumonia Nash9M19000bronchitis Alice12F22000flu Full identification information

15. Rule based knowledge Atom A i  a predicate about a person and his/her sensitive values  t Jack [Disease] = flu says that the Jack’s tuple has the value flu for the sensitive attribute Disease. Basic implication Background knowledge  formulated as conjunctions of k basic implications

16. The idea use k to bound the background knowledge, and compute the maximum disclosure of a bucket data set with respect to the background knowledge.

17. (vldb07-4) [Bee-Chung, et al. VLDB’07] use a triple (l, k,m) to specify the bound of the background rather than a single k

18. Introduction [Martin, et al. ICDE’07] limitation of using a single number k to bound background knowledge novel multidimensional approach quantifying an adversary’s external knowledge by a novel multidimensional approach

19. Problem formulation Pr(t has s | K, D*) data owner has a table of data (denoted by D) data owner publishes the resulting release candidate D* S:a sensitive attribute s:a target sensitive value t:a target individual new bound specifies that  adversaries know l other people’s sensitive value;  adversaries know k sensitive values that the target does not have  adversaries know a group of m−1 people who share the same sensitive value with the target

20. Theoretical framework

21. (sigmod08-4) [Wenliang, et al. SIGMOD’08]

22. Introduction The impact of background knowledge:  How does it affect privacy?  How to measure its impact on privacy? Integrate background knowledge in privacy quantification.  Privacy-MaxEnt: A systematic approach.  Based on well-established theories. maximum entropy estimate

23. Challenges Directly computing P( S | Q ) is hard. What do we want to compute?  P( S | Q ), given the background knowledge and the published data set.

24. Our Approach Background Knowledge Published Data Public Information Constraints on x Constraints on x Solve x Consider P( S | Q ) as variable x (a vector). Most unbiased solution

25. Maximum Entropy Principle “ Information theory provides a constructive criterion for setting up probability distributions on the basis of partial knowledge, and leads to a type of statistical inference which is called the maximum entropy estimate. It is least biased estimate possible on the given information. ” — by E. T. Jaynes, 1957.

26. The MaxEnt Approach Background Knowledge Published Data Public Information Constraints on P( S | Q ) Constraints on P( S | Q ) Estimate P( S | Q ) Maximum Entropy Estimate

27. Entropy Because H(S | Q, B) = H(Q, S, B) – H(Q, B) Constraint should use P(Q, S, B) as variables

28. Maximum Entropy Estimate Let vector x = P(Q, S, B). Find the value for x that maximizes its entropy H(Q, S, B), while satisfying  h 1 (x) = c 1, …, h u (x) = c u : equality constraints  g 1 (x) ≤ d 1, …, g v (x) ≤ d v : inequality constraints A special case of Non-Linear Programming.

29. Putting Them Together Background Knowledge Published Data Public Information Constraints on P( S | Q ) Constraints on P( S | Q ) Estimate P( S | Q ) Maximum Entropy Estimate Tools: LBFGS, TOMLAB, KNITRO, etc.

30. Conclusion Privacy-MaxEnt is a systematic method  Model various types of knowledge  Model the information from the published data  Based on well-established theory.

31. (kdd08-2) [Srivatsava, et al. KDD’08]

32. Introduction reason about privacy in the face of rich, realistic sources of auxiliary information. investigate the effectiveness of current anonymization schemes in preserving privacy when multiple organizations independently release anonymized data present a composition attacks  an adversary uses independently anonymized releases to breach privacy

34. Summary What is background knowledge?  Probability-Based Knowledge P (s | q) = 1. P (s | q) = 0. P (s | q) = 0.2. P (s | Alice) = 0.2. 0.3 ≤ P (s | q) ≤ 0.5. P (s | q 1 ) + P (s | q 2 ) = 0.7  Logic-Based Knowledge (proposition/ first order/ modal logic) One of Alice and Bob has “Lung Cancer”.  Numerical data 50K ≤ salary of Alice ≤ 100K age of Bob ≤ age of Alice  Linked data degree of a node topology information ….  Domain Knowledge mechanism or algorithm of anonymization for data publication independently released anonymized data by other organizations  And many many others ….

35. Summary How to represent background knowledge?  Probability-Based Knowledge P (s | q) = 1. P (s | q) = 0. P (s | q) = 0.2. P (s | Alice) = 0.2. 0.3 ≤ P (s | q) ≤ 0.5. P (s | q 1 ) + P (s | q 2 ) = 0.7  Logic-Based Knowledge (proposition/ first order/ modal logic) One of Alice and Bob has “Lung Cancer”.  Numerical data 50K ≤ salary of Alice ≤ 100K age of Bob ≤ age of Alice  Linked data degree of a node topology information ….  Domain Knowledge mechanism or algorithm of anonymization for data publication independently released anonymized data by other organizations  And many many others …. [Martin, et al. ICDE’07]Rule-based [Wenliang, et al. SIGMOD’08] [Srivatsava, et al. KDD’08] [Raymond, et al. VLDB’07] general knowledge framework too hard to give a unified framework and give a general solution

36. Summary How to quantify background knowledge?  by the number of basic implications(association rules)  by a novel multidimensional approach  formulated as linear constraints How one can reason about privacy in the presence of external knowledge?  quantify the privacy  quantify the degree of randomization required  quantify the precise effect of background knowledge [Charu ICDE’07] [Martin, et al. ICDE’07] [Wenliang, et al. SIGMOD’08] [Bee-Chung, et al. VLDB’07] [Martin, et al. ICDE’07] [Wenliang, et al. SIGMOD’08]

37. Questions? Thanks to Zhiwei Li