Privacy-preserving set operations
Lea Kissner,Dawn Song +1 more
- pp 241-257
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TLDR
By building a framework of multiset operations, employing the mathematical properties of polynomials, this work designs efficient, secure, and composable methods to enable privacy-preserving computation of the union, intersection, and element reduction operations.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.read more
Citations
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Data Mining: The Textbook
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Secure Multiparty Computation for Privacy-Preserving Data Mining
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TL;DR: In this article, the basic paradigms and notions of secure mul-tiparty computation and their relevance to the field of privacy-preserving data mining are surveyed and discussed, as well as the relationship between secure multiparty computations and privacy preserving data mining.
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Location Privacy via Private Proximity Testing.
TL;DR: This work describes several secure protocols that support private proximity testing at various levels of granularity and studies the use of “location tags” generated from the physical environment in order to strengthen the security of proximity testing.
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