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Privacy workgroup

Privacy workgroup. Participants. Ashwin Machanavajjhala (leader) Suman Nath (scribe) Kristen Lefevre Evimaria Terzi Alan Mislove Ranga Raju Vatsavai Jennifer Neville Hakan Hacigumus Mohamed Mokbel . Various Facets. Data security Data privacy: Secret but useful Data compliance

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Privacy workgroup

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  1. Privacy workgroup

  2. Participants • AshwinMachanavajjhala (leader) • Suman Nath (scribe) • Kristen Lefevre • EvimariaTerzi • Alan Mislove • RangaRajuVatsavai • Jennifer Neville • HakanHacigumus • 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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