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Privacy workgroup. Participants Ashwin Machanavajjhala (leader) Suman Nath (scribe) Kristen Lefevre Evimaria Terzi Alan Mislove Ranga Raju Vatsavai Jennifer.

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Presentation on theme: "Privacy workgroup. Participants Ashwin Machanavajjhala (leader) Suman Nath (scribe) Kristen Lefevre Evimaria Terzi Alan Mislove Ranga Raju Vatsavai Jennifer."— Presentation transcript:

1 Privacy workgroup

2 Participants Ashwin Machanavajjhala (leader) Suman Nath (scribe) Kristen Lefevre Evimaria Terzi Alan Mislove Ranga Raju Vatsavai Jennifer Neville Hakan Hacigumus Mohamed Mokbel

3 Various Facets Data security Data privacy: Secret but useful Data compliance User facing: how to specify privacy, do they understand? Avoid surprise Trust:

4 Privacy Desummarization of data, reverse of clustering/aggregation Examples: – Social: facebook releases statistics, fb makes friends suggestions, personalized recommendation/ads based on friends' likes – Mobile: publish mobility traces, or aggregates Issues: – Information propagation through links: S, through correlation of contexts: M – Information granularity small : S – # entities accessing data is large : CSM – Sparser data, higher dimensions: unique for individuals : SM – Multiple owners of data : CS – Different access control policies for different people, different context: SM – Unstructured data: text/speech/pictures: makes access control harder: SM – Location privacy: M

5 Data compliance Many formal verification problems: not our area We can help implementing efficiently in system, auditing, ensuring policies are implemented right Issues: C – Complexity of auditing diverse systems – Flow of information through multiple parties: compliance (Zynga using data through fb) – Forget : what if index/models have been built from data – Corporations can by each other – Apps contain third party libraries accessing private data – Do we need mandatory access control

6 User Facing How to get informed consent? Issues: – Users are content manager: SM – Number of decisions is large: share to whom, what context, at what granularity (goal: reduce number of decisions, make the process more intuitive): SM – Unreadable TOS: C (PL?) – Misinterpreting apps as the platform: C (HCI?) – Users don’t understand ease of access of data: CS (HCI?) – Accountability/understandability in model (recommendations/etc): SM (Mining?) – What can you learn about me? As a friend, as a random person? (by crowdsourcing?) S (ML?)


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