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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

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Summary :

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 [4] which assumed a mediate party to help in the computation.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.8 pp.1868-1871
Publication Date
2009/08/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.E92.A.1868
Type of Manuscript
Special Section LETTER (Special Section on Discrete Mathematics and Its Applications)
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