IEICE TRANSACTIONS on Fundamentals
Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party
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)
- Category
Authors
Keyword
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Chunhua SU, Feng BAO, Jianying ZHOU, Tsuyoshi TAKAGI, Kouichi SAKURAI, "Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 8, pp. 1868-1871, August 2009, doi: 10.1587/transfun.E92.A.1868.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.1868/_p
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@ARTICLE{e92-a_8_1868,
author={Chunhua SU, Feng BAO, Jianying ZHOU, Tsuyoshi TAKAGI, Kouichi SAKURAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party},
year={2009},
volume={E92-A},
number={8},
pages={1868-1871},
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.},
keywords={},
doi={10.1587/transfun.E92.A.1868},
ISSN={1745-1337},
month={August},}
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TY - JOUR
TI - Distributed Noise Generation for Density Estimation Based Clustering without Trusted Third Party
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1868
EP - 1871
AU - Chunhua SU
AU - Feng BAO
AU - Jianying ZHOU
AU - Tsuyoshi TAKAGI
AU - Kouichi SAKURAI
PY - 2009
DO - 10.1587/transfun.E92.A.1868
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2009
AB - 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.
ER -
English
