Data and Applications Security and Privacy XXVII: 27th - download pdf or read online

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

ISBN-10: 3642392555

ISBN-13: 9783642392559

ISBN-10: 3642392563

ISBN-13: 9783642392566

This booklet constitutes the refereed complaints of the twenty seventh IFIP WG 11.3 overseas convention on information and purposes safeguard and privateness, DBSec 2013, held in Newark, NJ, united states in July 2013. The sixteen revised complete and six brief papers provided have been rigorously reviewed and chosen from forty five submissions. The papers are geared up in topical sections on privateness, entry keep an eye on, cloud computing, facts outsourcing, and cellular computing.

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Read or Download Data and Applications Security and Privacy XXVII: 27th Annual IFIP WG 11.3 Conference, DBSec 2013, Newark, NJ, USA, July 15-17, 2013. Proceedings PDF

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Extra 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

Example text

P where {tj,1 , . . , tj,n } ⊆ x ∪ Dom for j ∈ {1, . . , p} and {tp+1,1 , . . , tp+1,n } ⊆ x ∪ y ∪ Dom. To demonstrate Ir |=M Γ , we inspect any variable substitution σ : x → Dom. If there exists j ∈ {1, . . , p} such that Irσ |=M ¬R(tj,1 , . . , tj,n ), we are done. Otherwise, for all j ∈ {1, . . , p} we have Irσ |=M ¬R(tj,1 , . . , tj,n ) and thus for each tuple μj := (σ (tj,1 ), . . , σ (tj,n )) we have μj ∈ r . Since r := ϕ∗ [r], for all j ∈ {1, . . , p} there exists μj ∈ r such that ϕ∗ [μj ] = μj .

Vk ). By construction, this completeness sentence is satisfied by any DB-Interpretation induced by f1 . Example 6. For the medical example, the knowledge implicitly taken to be not valid by CWA can be expressed as the following completeness sentence: (∀Xt )(∀XS )(∀XN )(∀XH )(∀XD ) [ (Xt ≡ 1 ∧ XS ≡ e1S ∧ XN ≡ Hellmann (Xt ≡ 2 ∧ XS ≡ e2S ∧ XN ≡ Dooley (Xt ≡ 3 ∧ XS ≡ e3S ∧ XN ≡ McKinley (Xt ≡ 4 ∧ XS ≡ e4S ∧ XN ≡ McKinley ¬F1 (Xt , XS , XN , XH , XD ) ∧ ∧ ∧ ∧ XH XH XH XH ≡ e1H ≡ e2H ≡ e3H ≡ e4H ∧ ∧ ∧ ∧ XD XD XD XD ≡ White) ≡ Warren) ≡ Warren) ≡ Warren) ∨ ∨ ∨ ∨ ] Based on the explanations given so far, an attacker’s knowledge about the fragment instance f1 can be formalized logic-orientedly as follows: Definition 5 (Logic-Oriented View on f1 ).

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 }. g. 0 s. Here we set δ = 1 throughout the entire time-series for all cells. 2 Differential Privacy The privacy guarantee provided by our solutions is differential privacy [1]. Simply put, a mechanism is differentially private if its outcome is not significantly affected by the removal or addition of a single user. An adversary thus learns approximately the same information about any individual user, irrespective of his/her presence or absence in the original database.

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Data and Applications Security and Privacy XXVII: 27th Annual IFIP WG 11.3 Conference, DBSec 2013, Newark, NJ, USA, July 15-17, 2013. Proceedings by Sabrina De Capitani di Vimercati, Sara Foresti, Sushil Jajodia, Giovanni Livraga (auth.), Lingyu Wang, Basit Shafiq (eds.)


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