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

• SU Chunhua

  Dept. of Computer Science and Communication Engineering, Kyushu University

• BAO Feng

  Cryptography and Security Department, Institute for Infocomm Research

• ZHOU Jianying

  Cryptography and Security Department, Institute for Infocomm Research

• TAKAGI Tsuyoshi

  School of Systems Information Science, Future University-Hakodate

• SAKURAI Kouichi

  Dept. of Computer Science and Communication Engineering, Kyushu University

Abstract

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.

Journal

• IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

  IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E92-A (8), 1868-1871, 2009

  The Institute of Electronics, Information and Communication Engineers

Keywords

• data clustering
• privacy-preserving
• random data perturbation

Details

• CRID: 1390282681288095616
• NII Article ID: 10026858826
• NII Book ID: AA10826239
• DOI: 10.1587/transfun.e92.a.1868
• ISSN: 17451337
• ISSN: 09168508
• Web Site: http://www.jstage.jst.go.jp/article/transfun/E92.A/8/E92.A_8_1868/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed