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 e#cient techniques for privacy-preserving operations on multisets. By employing the mathematical properties of polynomials, we build a framework of efficient, secure, and composable multiset operations: the union, intersection, and element reduction operations. We apply these techniques to a wide range of...
Private set intersection (PSI) allows participants to securely compute the intersection of their inp...
Private Set Intersection protocols (PSIs) allow parties to compute the intersection of their private...
This study concentrates on preserving privacy in a network of agents where each agent desires to eva...
In many important applications, a collection of mutually distrustful parties must perform private co...
In many important applications, a collection of mutually distrustful parties must perform private c...
Privacy-preserving set operations, and set intersection in particular, are a popular research topic....
Abstract Privacy-preserving set operations, and set intersection in particular, are a popular resear...
In our increasingly digital society, we are making a growing amount of data available to computers, ...
Abstract. We study the design of efficient and private protocols for polynomial operations in the sh...
This study concentrates on preserving privacy in a network of agents where each agent seeks to evalu...
This study concentrates on preserving privacy in a network of agents where each agent seeks to evalu...
Abstract. Privacy preserving multiset union (PPMU) protocol allows a set of parties, each with a mul...
Privacy-preserving set operations are useful for many data mining algorithms as building tools. Prot...
Secure computation of the set intersection functionality allows $n$ parties to find the intersection...
In this paper we focus on protocols for private set intersection (PSI), through which two parties, e...
Private set intersection (PSI) allows participants to securely compute the intersection of their inp...
Private Set Intersection protocols (PSIs) allow parties to compute the intersection of their private...
This study concentrates on preserving privacy in a network of agents where each agent desires to eva...
In many important applications, a collection of mutually distrustful parties must perform private co...
In many important applications, a collection of mutually distrustful parties must perform private c...
Privacy-preserving set operations, and set intersection in particular, are a popular research topic....
Abstract Privacy-preserving set operations, and set intersection in particular, are a popular resear...
In our increasingly digital society, we are making a growing amount of data available to computers, ...
Abstract. We study the design of efficient and private protocols for polynomial operations in the sh...
This study concentrates on preserving privacy in a network of agents where each agent seeks to evalu...
This study concentrates on preserving privacy in a network of agents where each agent seeks to evalu...
Abstract. Privacy preserving multiset union (PPMU) protocol allows a set of parties, each with a mul...
Privacy-preserving set operations are useful for many data mining algorithms as building tools. Prot...
Secure computation of the set intersection functionality allows $n$ parties to find the intersection...
In this paper we focus on protocols for private set intersection (PSI), through which two parties, e...
Private set intersection (PSI) allows participants to securely compute the intersection of their inp...
Private Set Intersection protocols (PSIs) allow parties to compute the intersection of their private...
This study concentrates on preserving privacy in a network of agents where each agent desires to eva...