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

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概要

• 論文の詳細を見る

• 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.

• （社）電子情報通信学会の論文
• 2009-08-01

著者

• SAKURAI Kouichi

  Dept. of Computer Science and Communication Engineering, Kyushu University

• Sakurai Kouichi

  Kyushu Univ. Fukuoka‐shi Jpn

• Sakurai Kouichi

  Dept. Of Computer Science And Communication Engineering Kyushu University

• Takagi Tsuyoshi

  Future Univ.‐hakodate Hakodate‐shi Jpn

• Takagi Tsuyoshi

  School Of Systems Information Science Future University-hakodate

• Sakurai Kouichi

  Information Technology And Security Group Department Of Computer Science And Communication Engineeri

• 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

• Bao Feng

  Cryptography And Security Department Institute For Infocomm Research

• Su Chunhua

  Dept. Of Computer Science And Communication Engineering Kyushu University

• Zhou Jianying

  Cryptography And Security Department Institute For Infocomm Research

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