Abstract. We consider scenarios in which two parties, each in possession of a graph, wish to compute some algorithm on their joint graph in a privacy-preserving manner, that is, wi...
In this paper we introduce a framework for privacypreserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy ...
Yitao Duan, NetEase Youdao, John Canny, Justin Z. ...
—In this work we study the problem of query privacy in large scale sensor networks. Motivated by a novel trust model in which clients query networks owned by trusted entities but...
Preservation of privacy in micro-data release is a challenging task in data mining. The k-anonymity method has attracted much attention of researchers. Quasiidentifier is a key co...
Abstract. This paper introduces an efficient privacy-preserving protocol for distributed K-means clustering over an arbitrary partitioned data, shared among N parties. Clustering i...
Maneesh Upmanyu, Anoop M. Namboodiri, Kannan Srina...