Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party

Chunhua SU  Feng BAO  Jianying ZHOU  Tsuyoshi TAKAGI  Kouichi SAKURAI  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E92-A   No.8   pp.1868-1871
Publication Date: 2009/08/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E92.A.1868
Print ISSN: 0916-8508
Type of Manuscript: Special Section LETTER (Special Section on Discrete Mathematics and Its Applications)
Category: 
Keyword: 
data clustering,  privacy-preserving,  random data perturbation,  

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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.