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Quantifying Location Privacy

Quantifying Location Privacy. Reza Shokri , George Theodorakopoulos , Jean-Yves Le Boudec and Jean-Pierre Hubaux. About. This paper was written in 2011 , published on IEEE S&P . It counts a significant part in Reza Shokri’s PhD thesis.

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Quantifying Location Privacy

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  1. Quantifying Location Privacy Reza Shokri, George Theodorakopoulos, Jean-Yves Le Boudec and Jean-Pierre Hubaux

  2. About • This paper was written in 2011, published on IEEE S&P. • It counts a significant part in Reza Shokri’s PhD thesis. • It is recognized as the runner-up for PET Award 2012. • Slides on Prezi: http://prezi.com/w5oogou4ypkl/quantifying-and-protecting-location-privacy/

  3. In this paper • A systematic framework to quantify location privacy protection mechanism (LPPM). • Modeling LPPM • Modeling prior information • Modeling attacks • Claiming correctness is the right metric for user’s privacy. • Certainty? • Accuracy? • Correctness? • Implementing a tool: Location-Privacy Meter. • Assessing the appropriateness of Entropy metric and k-anonymity.

  4. What is Location Privacy • Has been a concern with the rise of Location based services (LBS). • Examples of LBS: • Examples of concerns:

  5. Location Privacy is IMPORTANT • A bunch of points on map? • A trace? • It reveals your habits, interests, relationship and more!

  6. How to protect • By LPPM (Location privacy protection mechanism). • Anonymization • Location/path obfuscation • Perturbation: 12 19 • Dummy: 12 {12, 19, 4, 27} • Reducing precision: 14 Teens • Location hiding: 12

  7. How to attack • Where is s/he at this time? • Track him/her! • Who meets whom at a given place/time? Presence/absence disclosure attack Meeting disclosure attack

  8. Big question: How to model? • A framework : set of mobile users : set of possible actual traces LPPM: location privacy protection mechanism : set of observed traces ADV: adversary METRIC: evaluation metric

  9. Elements of Framework

  10. Modeling Users: event and trace • User moves within Region at time T • U – R – T • An event is defined as a triple • A trace of user u is a T-size vector or event • : the set of all traces that may belong to user. • x x … x : the set of all possible traces of all possible users

  11. Modeling LPPM • An LPPM receives a set of N actual traces, one for each user, and modifies them in two steps: • Obfuscation process: location nyms • An obfuscation mechanism: • Anonymization process: user nyms • In this paper: random permutation • LPPM: ()

  12. LPPM Attacker

  13. Modeling adversary • An adversary is characterized by • Knowledge • Attack • Knowledge • Training traces (incomplete and/or noisy) • Public information • Observable traces released by LPPM • Attack • Presence/absence disclosure attack • Meeting disclosure attack • See details in III.B, III.C }

  14. Which metric for user privacy? • The answer of an attack is not deterministic, but probabilistic, due to the mechanism of LPPM. • Expecting a distribution as the result of attack, so we have: • Uncertainty of a distribution • Accuracy of each element on the distribution • Correctness: distance between the truth and attacker’s estimation.

  15. Which metric for user privacy? • Uncertainty: • Given a distribution, each element seems so uniform! • Uncertainty is high / certainty is low. • Prob. distribution <0.33, 0.33, 0.33>

  16. Which metric for user privacy? • Uncertainty • If someone is outstanding • Uncertainty is low / Certainty is high • Prob. distribution <0.1, 0.1, 0.8>

  17. Which metric for user privacy? • Accuracy is quantified with confidence interval and confidence level.

  18. Which metric for user privacy? • Correctness is what users really care. • User: I do not care your uncertainty. • Even if I am outstanding in your distribution (high certainty), you may not get me right. (E.g. I am @ starbucks while you say am @ McD) • User: I do not care your accuracy. • Even if your are accurate enough, you may still not get me right. • User: what I care is whether you get me right, i.e., correctness.

  19. Uncertainty, accuracy and correctness

  20. So far • Framework for LPPM • Modeling LPPM () • Modeling prior information Not covered • Modeling attacks Not covered • Right Metric • Certainty • Accuracy • Correctness • Implementing a tool: Location-Privacy Meter. • Assessing Entropy metric and k-anonymity.

  21. Implementing Location-Privacy Meter • Modeling prior information • User’s mobility can be modeled as Markov Chain • So, we need transition matrix for MC. • The mobility profile is the transition matrix. • We need !

  22. Approaching

  23. Approaching , statistics details • Gibbs sampling for • Iterative procedure • Dirichlet prior for each row in • Iterative procedure for ET

  24. Implementing Location-Privacy Meter • Modeling attack • Maximum likelihood attack • to find the jointly most likely traces for all users • Distribution tracking attack • Computes the distribution of traces for each user

  25. Attack, algorithm details • Maximum Weight Assignment (MWA) • Hungarian algorithm • Viterbi algorithm • Metropolis Hastings (MH) algorithm

  26. Assessing Entropy metric and k-anonymity

  27. Assessing Entropy metric and k-anonymity

  28. Questions?

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