Download PDF by Konrad Leszczyński, Maciej Zakrzewicz (auth.), Steven: Trust, Privacy and Security in Digital Business: 8th

By Konrad Leszczyński, Maciej Zakrzewicz (auth.), Steven Furnell, Costas Lambrinoudakis, Günther Pernul (eds.)

ISBN-10: 3642228895

ISBN-13: 9783642228896

ISBN-10: 3642228909

ISBN-13: 9783642228902

This publication constitutes the refereed court cases of the eighth foreign convention on belief and privateness in electronic enterprise, TrustBus 2011, held in Toulouse, France, in August/September 2011 along side DEXA 2011. The 18 revised complete papers provided have been conscientiously reviewed and chosen from quite a few submissions. The papers are equipped within the following topical sections: id and belief administration; safeguard and privateness types for pervasive info structures; reliability and safeguard of content material and information; authentication and authorization in electronic company; intrusion detection and knowledge filtering; administration of privateness and confidentiality; and cryptographic protocols/usability of security.

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Read or Download Trust, Privacy and Security in Digital Business: 8th International Conference, TrustBus 2011, Toulouse, France, August 29 - September 2, 2011. Proceedings PDF

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Extra info for Trust, Privacy and Security in Digital Business: 8th International Conference, TrustBus 2011, Toulouse, France, August 29 - September 2, 2011. Proceedings

Sample text

Definition 5. -Differential privacy [12]: A randomized function K gives -differential privacy if for all data sets D1 and D2 differing on at most one element, and all S ⊆ Range(K), the following holds: P r[K(D1 ) ∈ S] ≤ exp( ) × P r[K(D2 ) ∈ S] The probability is taken is over the coin tosses of K. In our solution, the differential privacy is meaningful only if the SAS may find out the identities of participants in the distributed computation. Otherwise, the privacy is guaranteed by k-anonymity. If the SAS knows or may find out the identify of participants, then the concept of differential privacy applies and the common technique to assure differential privacy is to add appropriate additive random noise to the results [13].

On k-anonymity and the curse of dimensionality. In: VLDB 2005, pp. 901–909 (2005) 3. org/ 4. : Capturing personal health data from wearable sensors. In: SAINT 2008, pp. 153–156. IEEE, Los Alamitos (2008) 5. : κ-anonymity. In: Secure Data Management in Decentralized Systems. Advances in Information Security, vol. 33, pp. 323–353. Springer, Heidelberg (2007) 6. : Privacy-enhanced management of ubiquitous health monitoring data. In: PETRA 2011. ACM, New York (2011) 7. : A privacy-preserving protocol for finding the nearest doctor in an emergency.

Differential privacy: a survey of results. , Li, A. ) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008) 13. : Calibrating noise to sensitivity in private data analysis. , Rabin, T. ) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006) 14. : Towards privacy in personal data management. J. IMCS 17(4), 311–329 (2009) 15. : Privacy-preserving data mining in the malicious model. Int. J. IJICS 2(4), 353–375 (2008) 36 G. S. Efraimidis 16. : Privacy and anonymization for very large datasets.

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Trust, Privacy and Security in Digital Business: 8th International Conference, TrustBus 2011, Toulouse, France, August 29 - September 2, 2011. Proceedings by Konrad Leszczyński, Maciej Zakrzewicz (auth.), Steven Furnell, Costas Lambrinoudakis, Günther Pernul (eds.)


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