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Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data. We conduct experiments to compare the accuracy of our decision tree with the one built(More)
Secure Multi-party Computation (SMC) problems deal with the following situation: Two (or many) parties want to jointly perform a computation. Each party needs to contribute its private input to this computation, but no party should disclose its private inputs to the other parties, or to any third party. With the proliferation of the Internet, SMC problems(More)
In this paper we introduce a framework for privacy-preserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy (P4P) and features a novel heterogeneous architecture and a number of efficient tools for performing private computation and ensuring security at large scale. It maintains the(More)
— One of the most substantial ways to protect users' sensitive information is encryption. This paper is about the keyword index search system on encrypted documents. It has been thought that the search with errors over encrypted data is impossible because 1 bit difference over plaintexts may reduce to enormous bits difference over cyphertexts. We propose a(More)
Secure multiparty computation is a very important research topic in cryptography. The sub-problem of secure multiparty computation that has received special attention by researchers because of its close relation to many cryptographic tasks is secure two-party computation. This area of research is concerned with the question: " Can two party computation be(More)