Data and Applications Security and Privacy XXX: 30th Annual by Silvio Ranise, Vipin Swarup

By Silvio Ranise, Vipin Swarup

This ebook constitutes the refereed lawsuits of the thirtieth Annual IFIP WG 11.3 foreign operating convention on information and functions safety and privateness, DBSec 2016, held in trento, Itlay, in July 2016. The 17 complete papers and seven brief papers awarded have been rigorously reviewed and chosen from fifty four submissions. Their issues hide quite a lot of facts and alertness safeguard and privateness difficulties together with these of cellular units, collaborative structures, databases, enormous information, digital platforms, cloud computing, and social networks. this system additionally incorporated twoinvited talks.

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Extra info for Data and Applications Security and Privacy XXX: 30th Annual IFIP WG 11.3 Conference, DBSec 2016, Trento, Italy, July 18-20, 2016. Proceedings

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It is specifically designed c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved S. Ranise and V. ): DBSec 2016, LNCS 9766, pp. 37–51, 2016. 1007/978-3-319-41483-6 3 38 L. Ferretti et al. to work efficiently in read and append-only workloads possibly characterized by bulk operations, where large amounts of records may be inserted in the key-value database through one write operation. Moreover, Bulkopt supports efficient finegrained data retrievals by reducing network overhead related to the verification of bulk read operations in which multiple, possibly dispersed, keys are retrieved at once.

For this reason, we propose to use the infinity norm to compute the optimal coupling between trajectories, yet we consider the average Manhattan norm to represent the actual distance between them. We claim that the average Manhattan norm approximates better the required distortion to microaggregate trajectories. Formally, the distance measure used in the present article is defined as follows. Definition 2 (Fr´ echet/Manhattan coupling distance). Let U = u1 · · · up and V = v1 · · · vq be two trajectories and let L be the set of all couplings between U and V .

6]. However, they target privacy notions different to k-anonymity. Like Terrovitis and Mamoulis in [20], Yarovoy et al. also consider an adversary controlling a subset of user’s locations or quasi-identifiers [25], with the distinction that such a subset may differ for different users. Trajectory k-anonymity is defined in terms of a bipartite attack graph relating original trajectories with the anonymised trajectories. The authors propose to create anonymised groups through generalisation with respect to the joint set of quasi-identifiers from the users within the group.

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