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

By Silvio Ranise, Vipin Swarup

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

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Read or Download Data and Applications Security and Privacy XXX: 30th Annual IFIP WG 11.3 Conference, DBSec 2016, Trento, Italy, July 18-20, 2016. Proceedings PDF

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

Sample text

2) and read operations (Sect. 3). 1 43 Setup and Key Generation Setup. Let g be a generator of the cyclic multiplicative group G of prime order p, GT a cyclic multiplicative group of the same order and eˆ : G × G → GT be the pairing function that satisfies the following properties: bilinearity: eˆ(ma , nb ) = ab eˆ(m, n) ∀m, n ∈ G, a, b ∈ Z∗p ; non-degeneracy: eˆ(g, g) = 1; computability: there exists an efficient one-way algorithm to compute eˆ(m, n), ∀m, n ∈ G. Let h be a cryptographic hash function and hz (·), hg (·) be two full domain hash functions (FDH) secure in the random oracle model [2,7] defined as following: ∗ hz : {0, 1} → Z∗p (10) hg : {0, 1} → G (11) ∗ Let us denote as CM (s) the characteristic polynomial that uniquely represents the set M , generated by using as roots of the polynomial the sum opposite of the elements of the set and as variable the secret key s [22].

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.

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.

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