By Panayiotis Kotzanikolaou, Emmanouil Magkos, Nikolaos Petrakos, Christos Douligeris (auth.), Roberto Di Pietro, Javier Herranz, Ernesto Damiani, Radu State (eds.)

ISBN-10: 3642358896

ISBN-13: 9783642358890

ISBN-10: 364235890X

ISBN-13: 9783642358906

This ebook constitutes the completely refereed joint submit complaints of 2 foreign workshops, the seventh overseas Workshop on facts privateness administration, DPM 2012, and the fifth overseas Workshop on self reliant and Spontaneous safeguard, SETOP 2012, held in Pisa, Italy, in September 2012. the amount includes thirteen complete papers chosen out of 31 submissions and three keynote lectures from the DPM workshop and 10 papers chosen between a variety of submissions from the SETOP workshop. The contributions from DPM disguise themes from position privateness, electorate' privateness, privateness, authentication with anonymity, privateness in allotted platforms, privateness guidelines, and automatic privateness enforcement. The SETOP contributions offer a distinct view of ongoing safety learn paintings in a couple of rising environments which are changing into a part of the worldwide ICT infrastructure, from content-centric to cellular and instant networks. additionally, a few of them conceal the major position of run-time enforcement in strategy and repair protection. the themes of SETOP papers comprise: safety coverage deployment; dispensed intrusion detection; independent and spontaneous reaction; privateness rules; safe localization; context acutely aware and ubiquitous computing; identification management.

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Extra resources for Data Privacy Management and Autonomous Spontaneous Security: 7th International Workshop, DPM 2012, and 5th International Workshop, SETOP 2012, Pisa, Italy, September 13-14, 2012. Revised Selected Papers

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The notations used in this paper are summarised in Tab. 1. Table 1. Notations U L T LK TR fee(tr) cost u Tr u d(tr1 , tr2 ) set of users set of locations set of time points set of links set of trips trip tr fee user u’s toll payment a trace of user u distance between tr1 and tr2 Fig. 1. , trajectory pattern mining [3], location-based recommendation, car pooling and friend finder [16, 17]. In this paper, we assume that toll servers are malicious and collude with the adversary, which makes users’ location records and toll payment information part of the adversary’s knowledge.

With this choice of the threshold, the probability of missing a tag is ⎞ ⎛ √L − ⎟ ⎜ 2 Nt (22) Pm = Φ ⎝ 1/2 ⎠ L Nt −1 1 1 L + 2 k=1 γ (k) Nt ρ t where Φ(·) is the standard Gaussian cumulative distribution function. To get an idea of the theoretical performance of the scheme, let us look at a special case where the channel is flat fading with SNR = 10dB. e. e. L = 144. e. 01. So we can see under that condition, the tags are detected 99% of the time. 4 Simulations As mentioned in Section 2, the PDCCH messages are designed to be very robust.

Springer, Heidelberg (2012) 9. : The phantom tollbooth: Privacypreserving electronic toll collection in the presence of driver collusion. In: Proc. 20th USENIX Security Symposium. USENIX Association (2011) 10. : Robust de-anonymization of large sparse datasets. In: Proc. 29th IEEE Symposium on Security and Privacy (S&P), pp. 111–125. IEEE CS (2008) 11. : De-anonymizing social networks. In: Proc. 30th IEEE Symposium on Security and Privacy (S&P), pp. 173–187. IEEE CS (2009) 12. : A practical attack to de-anonymize social network users.

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Data Privacy Management and Autonomous Spontaneous Security: 7th International Workshop, DPM 2012, and 5th International Workshop, SETOP 2012, Pisa, Italy, September 13-14, 2012. Revised Selected Papers by Panayiotis Kotzanikolaou, Emmanouil Magkos, Nikolaos Petrakos, Christos Douligeris (auth.), Roberto Di Pietro, Javier Herranz, Ernesto Damiani, Radu State (eds.)


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