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Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party
Chunhua SU Feng BAO Jianying ZHOU Tsuyoshi TAKAGI Kouichi SAKURAI
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2009/08/01
Online ISSN: 1745-1337
Print ISSN: 0916-8508
Type of Manuscript: Special Section LETTER (Special Section on Discrete Mathematics and Its Applications)
data clustering, privacy-preserving, random data perturbation,
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The rapid growth of the Internet provides people with tremendous opportunities for data collection, knowledge discovery and cooperative computation. However, it also brings the problem of sensitive information leakage. Both individuals and enterprises may suffer from the massive data collection and the information retrieval by distrusted parties. In this paper, we propose a privacy-preserving protocol for the distributed kernel density estimation-based clustering. Our scheme applies random data perturbation (RDP) technique and the verifiable secret sharing to solve the security problem of distributed kernel density estimation in  which assumed a mediate party to help in the computation.