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. ...
Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each si...
The recent investigation of privacy-preserving data mining and other kinds of privacy-preserving distributed computation has been motivated by the growing concern about the privacy...
Hiranmayee Subramaniam, Rebecca N. Wright, Zhiqian...
Most previous research on privacy-preserving data publishing, based on the k-anonymity model, has followed the simplistic approach of homogeneously giving the same generalized val...
Wai Kit Wong, Nikos Mamoulis, David Wai-Lok Cheung
More and more applications rely heavily on large amounts of data in the distributed storages collected over time or produced by large scale scientific experiments or simulations. ...