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Probabilistic Privacy Analysis of Published Views

Probabilistic Privacy Analysis of Published Views. Hui (Wendy) Wang Laks V.S. Lakshmanan University of British Columbia Vancouver, Canada. Motivation. Publishing relational data containing personal information Sensitive information: private associations E.g., Bill gets AIDS

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Probabilistic Privacy Analysis of Published Views

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  1. Probabilistic Privacy Analysis of Published Views Hui (Wendy) Wang Laks V.S. Lakshmanan University of British Columbia Vancouver, Canada Probabilistic Privacy Analysis of Published Views, WPES'06

  2. Motivation • Publishing relational data containing personal information • Sensitive information: private associations • E.g., Bill gets AIDS • The published data • Usage: for data analysis • E.g. find out what are the ages that people are more likely to have heart disease • Privacy concern: hide the private association Probabilistic Privacy Analysis of Published Views, WPES'06

  3. Protection Approach 1 • Generalization of base table (k-anonymity, e.g., [Bayardo05], [LeFevre05]) • The generalized data is USELESS for data analysis! • Revisit the example: what are the ages that people are more likely to have heart disease Probabilistic Privacy Analysis of Published Views, WPES'06

  4. Protection Approach 2 • Publishing Views (E.g., [Yao05], [Deutch05], [Miklau04]) V1 V2 • Private associations may be revealed • E.g., V1 join V2 Prob(“Bill”, “AIDS”) = 1 Probabilistic Privacy Analysis of Published Views, WPES'06

  5. Problem Set Given a view scheme, what’s its probability of leakage of private association? Probabilistic Privacy Analysis of Published Views, WPES'06

  6. Our Contributions • Define two attack models • Propose connectivity graph as the synopsis of the database • Based on connectivity graph, for each attack model, derive the probability of information leakage Probabilistic Privacy Analysis of Published Views, WPES'06

  7. Security Model • Private association • Form: (ID=I, P=p) • E.g., (Name=“Bill”, Disease=“HIV”) • Can be expressed in SQL • Assumption • For every private association, every ID value is associated with one unique p value in the base table Probabilistic Privacy Analysis of Published Views, WPES'06

  8. Attack Model 1: Unrestricted Model • The attacker has no background knowledge • The attacker can access to the view def. and the view tables • The attack approach • Construct the candidates of base table • Pick the ones that contain the private association Probabilistic Privacy Analysis of Published Views, WPES'06

  9. Example of Unrestricted Model Attacker knows: V1= A, B (T) V2 = B, c (T) Base table T Attacker constructs: There are 7 such possible worlds ... Possible world #1 Possible world #2 Possible world #3 There are 5 such interesting worlds For (A=a1, C=c1), attacker picks: √ X √ Prob. of privacy breach of (A=a1, C=c1): 5/7 Probabilistic Privacy Analysis of Published Views, WPES'06

  10. Attack Model 2: Restricted Model • The attacker knows the assumption that for every private association, every ID value is associated with one unique p value in the base table • Similar approach • Construct the candidates of base table, s.t., they meet the assumption • Pick the ones that contain the private association Probabilistic Privacy Analysis of Published Views, WPES'06

  11. Example of Restricted Model Attacker knows: V1= A, B (T) V2 = B, c (T) Base table T For (A=a1, C=c1), Attacker constructs Possible world #1 Possible world #2 Possible world #3 Possible world #4 √ √ Attacker picks: X X Prob. of privacy breach of (A=a1, C=c1): 1/2 Probabilistic Privacy Analysis of Published Views, WPES'06

  12. A Further Step • Question • Given a view scheme and two view tables, how to efficiently calculate the probability? • Our contributions • For each attack model, we derived the formulas to quantify the probability • Details can be found in the paper Probabilistic Privacy Analysis of Published Views, WPES'06

  13. Conclusion • We defined a general framework to measure the likelihood of privacy breach • We proposed two attack models • For each model, we derived the formulas to calculate the probability of privacy breach Probabilistic Privacy Analysis of Published Views, WPES'06

  14. Future Work • For the formulas of calculation of the breach probability, find an appropriate approximation • Extend the work to k-view-table case, where k>2 Probabilistic Privacy Analysis of Published Views, WPES'06

  15. Q & A Probabilistic Privacy Analysis of Published Views, WPES'06

  16. More Slides Probabilistic Privacy Analysis of Published Views, WPES'06

  17. Example of Probability Calculation View scheme V1= A, B (T) V2 = B, c (T) Base table T 1 3 • Unrestricted model V.S. unrestricted cover • Example of unrestricted cover • Restricted model V.S. restricted cover • Example of restricted cover a1, b1 b1, c1 2 a2, b1 b1, c2 4 Connectivity graph Probabilistic Privacy Analysis of Published Views, WPES'06

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