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

Privacy-Sensitive Data Publication NameAgeZipcodeDisease Alex AIDS Bob cancer Carol flu AgeZipcodeDisease Under *AIDS Under *cancer 30 or above1234*flu AgeZipcodeDisease AIDS cancer flu Target’s sensitive value Attackers’ background knowledge is relevant to data publication de-identification generalization

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

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

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

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 Data Privacy Attacker Model

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

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

Model Details: Background Knowledge 3 categories of background knowledge: [Chen et al. ‘07] 1.(L) values that the target doesn’t have: Alex does not have cancer 2.(K) sensitive info about individuals different from the target Carol has flu 3.(M) relations between the target’s sensitive value and others’ If Carol has AIDS, Alex has AIDS

Model Details: Attackers 1.Agent space: n attackers, each is represented by its prior knowledge set: (K,L,M) 2.Action space: Each attacker decides how many and what instances to share (a k,a l,a m ) 1.Sharing mechanism: Pair-wise: direct exchange between every pair of attackers Reciprocal: exchange the same amount of information

Example Alex: no cancer Carol: flu Bob: cance r Carol: flu Bob: cance r

Normal-form Game Model Publisher chooses anonymization method Attackers choose how much and what information to share AFTER observing the anonymized data set Attackers choose how much and what information to share AFTER observing the anonymized data set Incomplete Information Game: KLMKLM akalamakalam Normal-form Game: KLMKLM akalamakalam Monte Carlo Sampling

Model Construction Overview Reason about attackers’ knowledge Evaluate anonymization choices

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: 1Attackers share all their knowledge 2No one shares 3Attackers play some Nash Equilibrium (NE)

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

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

Future Work Compact representations of prior knowledge [Chen et al ‘07] Incorporate more behavioral observations in reasoning about attackers’ behavior Non-game theoretic models (such as GMM [Duong et al ‘08])

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