Strategic Modeling of Information Sharing among Data Privacy Attackers

1. Strategic Modeling of Information Sharing among Data Privacy Attackers Quang Duong, Kristen LeFevre, and Michael Wellman University of Michigan Presented by: Quang Duong

2. Privacy-Sensitive Data Publication Target’s sensitive value de-identification generalization Attackers’ background knowledge is relevant to data publication

3. How Much Generalization? • Competing effects: • More generalization makes published data more resistant to privacy attackers • More generalization degrades information quality of published data • Need to model attackers’ background knowledge

4. Model of Privacy Attackers • Main difference: network of attackers who share background knowledge • Main contribution: a framework for constructing models that: • capture information sharing activities among attackers • estimate attackers’ background knowledge

5. Privacy Attacker Model’s Stages 1. ACQUIRE information separately 2. DECIDE how much and what to SHARE 3.ATTACK with their augmented knowledge

6. Data Privacy Attacker Model Decision: How much and what information to share Tradeoff (of sharing background knowledge): • Increase attack capability • Decrease compromised data’s exclusiveness Utility: • (number of successful attackers)-2 if capable of compromising the dataset • 0 otherwise

7. Database Publisher Model Decision: How much generalization should be applied to the published data Tradeoff (of generalizing data): • Reduce privacy breach risk • Induce more information loss Utility: (Linear) combination of privacy breach risk and information loss

8. Two-Stage Game Model Publisher decides how to generalize the data set 1st Attacker 1 Attacker 2 Attacker n 2nd … Choose how much and what to share  We can reason about the attackers’ actions and background knowledge, using different solution concepts such as Nash equilibrium

9. Model Details: Background Knowledge 3 categories of background knowledge: [Chen et al. ‘07] • (L) values that the target doesn’t have: Alex does not have cancer • (K) sensitive info about individuals different from the targetCarol has flu • (M) relations between the target’s sensitive value and others’ If Carol has AIDS, Alex has AIDS

10. Model Details: Attackers • Agent space: n attackers, each is represented by its prior knowledge set: (K,L,M) • Action space: Each attacker decides how many and what instances to share (ak,al,am) • Sharing mechanism: • Pair-wise: direct exchange between every pair of attackers • Reciprocal: exchange the same amount of information

11. Example Model – Empirical Study • Overview: • Data: 10 records, |domain of sensitive values| = 5 • Attackers: 3, each has 1 instance of each knowledge type • Publisher: explicitly specifies her generalization method  Construct and estimate the game’s payoff matrix • Testing scenarios: • Attackers share all their knowledge • No one shares • Attackers play some Nash Equilibrium (NE)

12. Outcomes under Different Attacker Action Scenarios • Publisher’s actions (I, II, III…): each has 3 data points corresponding to 3 attacker action scenarios. Each point corresponds to the publisher and attackers’ actions • Main result: the publisher may adopt different generalization strategies under different beliefs about attackers’ strategies

13. Concluding Remarks Contributions: • Propose a framework for reasoning about attackers’ actions • Initiate a game-theoretic study of privacy attackers as a knowledge-sharing network • Demonstrate that it matters to take into account attackers’ knowledge and their information-sharing activities

14. THANK YOU!