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Efficient Private Matching and Set Intersection
TLDR
This work considers the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain, and presents protocols, based on the use of homomorphic encryption and balanced hashing, for both semi-honest and malicious environments. Expand
Privacy Preserving Data Mining
TLDR
This work considers a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information, and proposes a protocol that is considerably more efficient than generic solutions and demands both very few rounds of communication and reasonable bandwidth. Expand
Efficient oblivious transfer protocols
TLDR
This paper presents several significant improvements to oblivious transfer protocols of strings, and in particular providing the first two-round OT protocol whose security analysis does not invoke the random oraclemodel. Expand
Oblivious transfer and polynomial evaluation
TLDR
The efficiency of the new OT protocols makes them useful for a variety of applications, including oblivious sampling which can be used to securely compare the sizes of web search engines, protocols for privately solving the list intersection problem and for mutually authenticated key exchange based on (possibly weak) passwords, and protocols for anonymity preserving web usage metering. Expand
Proofs of ownership in remote storage systems
TLDR
This work identifies 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, and introduces the notion of proofs-of-ownership (PoWs), which lets a client efficiently prove to a server that that the client holds a file, rather than just some short information about it. Expand
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
TLDR
This work presents an approach for watermarking Deep Neural Networks in a black-box way, and shows experimentally that such a watermark has no noticeable impact on the primary task that the model is designed for. Expand
Fairplay - Secure Two-Party Computation System
TLDR
Fairplay is introduced, a full-fledged system that implements generic secure function evaluation (SFE) and provides a test-bed of ideas and enhancements concerning SFE, whether by replacing parts of it, or by integrating with it. Expand
Secure Multiparty Computation for Privacy-Preserving Data Mining
TLDR
The issue of e-ciency is discussed and the di-cul- ties involved in constructing highly e-cient protocols are demonstrated and the relationship between secure multiparty computation and privacy-preserving data mining is discussed. Expand
Multicast security: a taxonomy and some efficient constructions
TLDR
A taxonomy of multicast scenarios on the Internet and an improved solution to the key revocation problem are presented, which can be regarded as a 'midpoint' between traditional message authentication codes and digital signatures. Expand
Privacy Preserving Data Mining
TLDR
This paper introduces the concept of privacy preserving data mining, and presents a solution that is considerably more efficient than generic solutions, and demonstrates that secure multi-party computation can be made practical, even for complex problems and large inputs. Expand
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