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

By Silvio Ranise, Vipin Swarup

This booklet constitutes the refereed court cases of the thirtieth Annual IFIP WG 11.3 overseas operating convention on info and functions safeguard and privateness, DBSec 2016, held in trento, Itlay, in July 2016. The 17 complete papers and seven brief papers offered have been conscientiously reviewed and chosen from fifty four submissions. Their issues disguise a variety of facts and alertness protection and privateness difficulties together with these of cellular units, collaborative structures, databases, vast information, digital structures, 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

Example text

The results are depicted in Fig. 1. It can be seen from Fig. 1 that our method performs better than the approaches proposed in [9,16] for every cluster size and every time interval. The improvement in terms of utility increases as the offered privacy increases. For k = 2, our method is just slightly better than the generalisation-based approach, while for k ∈ {4, 8} our method performs significantly better. This means that our technique clusters and anonymises trajectories more efficiently. Figure 1 also shows that more research on trajectory anonymisation techniques ought to be conducted.

20: end for 21: return D k In this article we consider trajectory k-anonymity as a property of the anonymised dataset regardless the adversary capabilities. Our notion of kanonymity is indeed similar to that presented in [15,16] for generalised trajectories. Definition 3 (Trajectory k-anonymity). Let D be a collection of trajectories. D meets trajectory k-anonymity if every trajectory in D is equal to other k − 1 trajectories in D . Theorem 1. Let D be a collection of original trajectories and D the output of Algorithm 3 on input D.

Y, denote the time, latitude, and longitude of the location , respectively. In general, trajectories can be recorded at different and irregular sampling rates, are not noise-free, and the velocity between two consecutive locations is assumed to be constant. A collection of trajectories is called a spatiotemporal database. For large databases, the size of a trajectory is considered to be significantly smaller than the size of the database. The choice of the distance measure is critical in microaggregation.

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