Data and Applications Security and Privacy XXVII: 27th by Sabrina De Capitani di Vimercati, Sara Foresti, Sushil

By Sabrina De Capitani di Vimercati, Sara Foresti, Sushil Jajodia, Giovanni Livraga (auth.), Lingyu Wang, Basit Shafiq (eds.)

This ebook constitutes the refereed court cases of the twenty seventh IFIP WG 11.3 overseas convention on info and functions protection and privateness, DBSec 2013, held in Newark, NJ, united states in July 2013. The sixteen revised complete and six brief papers offered have been rigorously reviewed and chosen from forty five submissions. The papers are geared up in topical sections on privateness, entry keep watch over, cloud computing, info outsourcing, and cellular computing.

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Additional resources for Data and Applications Security and Privacy XXVII: 27th Annual IFIP WG 11.3 Conference, DBSec 2013, Newark, NJ, USA, July 15-17, 2013. Proceedings

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First, to model the attacker’s knowledge about the fragment instance f1 , we need the predicate symbol F1 ∈ P with arity k + 1 = |AF1 | (including the additional tuple ID attribute plus k original attributes (cf. Fig. 2)). Second, to capture the attacker’s awareness of the fragmentation, in particular his partial knowledge about the hidden original instance r and the separated second fragmentation instance f2 , we additionally use the predicate symbols R with arity n = |AR | and F2 with arity n − h + 1 = |AF2 |.

Problem 1. Given a multi-dimensional time series XG where G = w × w cells, G for each snapshot XG k , release in real-time a sanitized version Rk such that the G overall release R satisfies α-differential privacy, where α is a user-specified privacy level. Note that sharing RG will enable a variety of data mining tasks. e. relative error, to measure the usefulness of the released series for each cell c: 36 L. Fan, L. Xiong, and V. Sunderam Definition 1 (Utility Metric). The utility of a published series Rc = {rkc } can be measured by the average relative error, denoted as E c , against the original time-series Xc ={xck }.

24 J. Biskup and M. Preuß Moreover the functional dependency atid → {a1 , . . , ak } ∈ SCF1 is modelled in L by the singleton set fd F1 containing the sentence (∀Xtid ) (∀X1 ) . . (∀Xk ) (∀X1 ) . . (∀Xk ) [ F1 (Xtid , X1 , . . , Xk ) ∧ F1 (Xtid , X1 , . . , Xk ) ⇒ (X1 ≡ X1 ) ∧ . . ∧ (Xk ≡ Xk ) ] . (3) − Overall the logic-oriented view on f1 in L is dbf1 := db+ f1 ∪ dbf1 ∪ fd F1 . Proposition 1. Under the assumptions of Def. , Ir |=M dbf1 . Proof. Direct consequence of the definitions. An attacker is assumed to know the process of fragmentation as well as the schemas R|AR |SCR and F2 |AF2 |SCF2 of the instances kept hidden from him.

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