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Detecting Data Leakage Panagiotis Papadimitriou papadimitriou@stanford.edu Hector Garcia-Molina hector@cs.stanford.edu

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Leakage Problem Stanford Infolab2 App. U 1 App. U 2 JeremySarahMark Other Sources e.g. Sarah’s Network Name: Mark Sex: Male …. Name: Sarah Sex: Female …. Kathryn

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Outline Problem Description Guilt Models – Pr{U 1 leaked data} = 0.7 – Pr{U 2 leaked data} = 0.2 Distribution Strategies Stanford Infolab3

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Problem Description Guilt Models Distribution Strategies Stanford Infolab4

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Problem Entities EntityDataset Distributor Facebook T Set of all Facebook profiles Agents Facebook Apps U 1, …, U n R 1, …, R n R i : Set of people’s profiles who have added the application U i Leaker S Set of leaked profiles Stanford Infolab5

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Agents’ Data Requests Sample – 100 profiles of Stanford people Explicit – All people who added application (example we used so far) – All Stanford profiles Stanford Infolab6

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Problem Description Guilt Models Distribution Strategies Stanford Infolab7

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Guilt Models (1/3) Stanford Infolab8 Other Sources e.g. Sarah’s Network 8 p p: posterior probability that a leaked profile comes from other sources p Guilty Agent: Agent who leaks at least one profile Pr{G i |S}: probability that agent U i is guilty, given the leaked set of profiles S

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Guilt Models (2/3) Stanford Infolab99 or Agents leak each of their data items independently Agents leak all their data items OR nothing or (1-p) 2 (1-p)p p(1-p) p2p2

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Guilt Models (3/3) IndependentlyNOT Independently Stanford Infolab10 Pr{G 1 } Pr{G 2 } Pr{G 1 }

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Problem Description Guilt Models Distribution Strategies Stanford Infolab11

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The Distributor’s Objective (1/2) Stanford Infolab12 U1U1 U1U1 U2U2 U2U2 U3U3 U3U3 U4U4 U4U4 Request R1R1 Pr{G 1 |S}>>Pr{G 2 |S} Pr{G 1 |S}>> Pr{G 4 |S} S (leaked) R1R1 R1R1 R3R3 R3R3 R2R2 R3R3 R4R4

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The Distributor’s Objective (2/2) To achieve his objective the distributor has to distribute sets R i, …, R n that minimize Intuition: Minimized data sharing among agents makes leaked data reveal the guilty agents Stanford Infolab13

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Distribution Strategies – Sample (1/4) Set T has four profiles: – Kathryn, Jeremy, Sarah and Mark There are 4 agents: – U 1, U 2, U 3 and U 4 Each agent requests a sample of any 2 profiles of T for a market survey Stanford Infolab14

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Distribution Strategies – Sample (2/4) Poor Minimize Stanford Infolab15 U1U1 U2U2 U3U3 U4U4 U1U1 U2U2 U3U3 U4U4

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Distribution Strategies – Sample (3/4) Optimal Distribution Avoid full overlaps and minimize Stanford Infolab16 U1U1 U2U2 U3U3 U4U4

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Distribution Strategies – Sample (4/4) Stanford Infolab17

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Distribution Strategies Sample Data Requests The distributor has the freedom to select the data items to provide the agents with General Idea: – Provide agents with as much disjoint sets of data as possible Problem: There are cases where the distributed data must overlap E.g., |R i |+…+|R n |>|T| Explicit Data Requests The distributor must provide agents with the data they request General Idea: – Add fake data to the distributed ones to minimize overlap of distributed data Problem: Agents can collude and identify fake data NOT COVERED in this talk Stanford Infolab18

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Conclusions Data Leakage Modeled as maximum likelihood problem Data distribution strategies that help identify the guilty agents Stanford Infolab19

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Thank You!

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