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Efficient Private Matching and Set Intersection
tl;dr
We consider the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain. Expand
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Privacy Preserving Data Mining
tl;dr
In this paper we address the issue of privacy preserving data mining. Expand
  • 1,832
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Efficient oblivious transfer protocols
tl;dr
This paper presents several significant improvements to oblivious transfer protocols of strings, and in particular:(i) Improving the efficiency of applications which many invocationsof oblivious transfer. (ii) Providing the first two-round OT protocol whose security analysis does not invoke the random oraclemodel. Expand
  • 715
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Oblivious transfer and polynomial evaluation
tl;dr
We describe efficient constructions for two oblivious twoparty computation problems: l-out-of-N Oblivious Transfer &d ‘Oblivious Poly&nial Evaluation. Expand
  • 610
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Proofs of ownership in remote storage systems
tl;dr
We identify attacks that exploit client-side deduplication, allowing an attacker to gain access to arbitrary-size files of other users based on a very small hash signatures of these files. Expand
  • 567
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Privacy Preserving Data Mining
tl;dr
In this paper we introduce the concept of privacy preserving data mining. Expand
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Multicast security: a taxonomy and some efficient constructions
tl;dr
Multicast communication is becoming the basis for a growing number of applications. Expand
  • 780
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Privacy preserving auctions and mechanism design
tl;dr
We suggest an architecture for executing protocols for auctions and, more generally, mechanism design by adding another party the auction issuer that generates the programs for computing the auctions but does not take an active part in the protocol. Expand
  • 705
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Fairplay - Secure Two-Party Computation System
tl;dr
This paper introduces Fairplay [28], a full-fledged system that implements generic secure function evaluation (SFE). Expand
  • 595
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Secure Multiparty Computation for Privacy-Preserving Data Mining
tl;dr
In this paper, we survey the basic paradigms and notions of secure mul- tiparty computation and discuss their relevance to the fleld of privacy-preserving data mining. Expand
  • 566
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