Privacy-Preserving Set Operations View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2005

AUTHORS

Lea Kissner , Dawn Song

ABSTRACT

In many important applications, a collection of mutually distrustful parties must perform private computation over multisets. Each party’s input to the function is his private input multiset. In order to protect these private sets, the players perform privacy-preserving computation; that is, no party learns more information about other parties’ private input sets than what can be deduced from the result. In this paper, we propose efficient techniques for privacy-preserving operations on multisets. By building a framework of multiset operations, employing the mathematical properties of polynomials, we design efficient, secure, and composable methods to enable privacy-preserving computation of the union, intersection, and element reduction operations. We apply these techniques to a wide range of practical problems, achieving more efficient results than those of previous work. More... »

PAGES

241-257

Book

TITLE

Advances in Cryptology – CRYPTO 2005

ISBN

978-3-540-28114-6
978-3-540-31870-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11535218_15

DOI

http://dx.doi.org/10.1007/11535218_15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039851897


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