Financial Cryptography and Data Security: 15th International by Rainer Böhme, Stefanie Pötzsch (auth.), George Danezis

By Rainer Böhme, Stefanie Pötzsch (auth.), George Danezis (eds.)

This publication constitutes the completely refereed post-conference lawsuits of the fifteenth foreign convention on monetary Cryptography and information defense, FC 2011, held in Gros Islet, St. Lucia, in February/March 2011. The sixteen revised complete papers and 10 revised brief papers awarded have been rigorously reviewed and chosen from sixty five preliminary submissions. The papers conceal all elements of securing transactions and platforms and have present examine targeting basic and utilized real-world deployments on all facets surrounding trade defense; in addition to on platforms safety and inter-disciplinary efforts.

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Additional resources for Financial Cryptography and Data Security: 15th International Conference, FC 2011, Gros Islet, St. Lucia, February 28 - March 4, 2011, Revised Selected Papers

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6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 8 million – or 24 million – botted PCs on the Internet? : Privacy and rationality in individual decision making. : Data breach investigations report. : Dissecting one click frauds. In: Proceedings of the Conference on Computer and Communications Security (CCS), Chicago, IL, pp. : An inquiry into the nature and causes of the wealth of internet miscreants. In: Proceedings of ACM Conference on Computer and Communications Security (CCS), Alexandria, VA, pp.

We measure the ability of LBSs to infer uPOIs by considering for each user i, the number of uPOIs correctly inferred. For every user i, we have: i PuP OI = Number of uPOIs correctly guessed Number of uPOIs (4) We also use the notion of Kullback-Leibler divergence [27] to measure the ability of the adversary to guess the probability of each user visiting specific uPOIs. For every user i, we have: P i (j) (5) DKL (Pvi ||Qiv ) = Pvi (j) log vi Qv (j) j where Pvi is the actual probability that user i visits specific uPOIs and Qiv is the probability guessed by the adversary.

We quantify the potential of these threats by carrying out an experimentation based on real mobility traces from two cities, one in Sweden and one in Switzerland. We show that LBS providers are able to uniquely identify users and accurately profile them based on a small number of location samples observed from the users. Users with a strong routine face higher privacy risk, especially if their routine does not coincide with that of others. To the best of our knowledge, this work is among the first to investigate the erosion of privacy caused by the sharing of location samples with LBSs using real mobility traces and to quantify the privacy risk.

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