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Author:

Gao Feng (Gao Feng.) | He Jingsha (He Jingsha.) (Scholars:何泾沙) | Lv Xin (Lv Xin.) | Zhang Feng (Zhang Feng.)

Indexed by:

Scopus SCIE CSCD

Abstract:

In network environments, before meaningful interactions can begin, trust may need to be established between two interactive entities in which an entity may ask the other to provide some information involving privacy. Consequently, privacy protection and trust establishment become important in network interactions. In order to protect privacy while facilitating effective interactions, we propose a trust-based privacy protection method. Our main contributions in this paper are as follows: (1) We introduce a novel concept of k-sensitive privacy as a measure to assess the potential threat of inferring privacy; (2) According to trust and k-sensitive privacy evaluation, our proposed method can choose appropriate interaction patterns with lower degree of inferring privacy threat; (3) By considering interaction patterns for privacy protection, our proposed method can overcome the shortcomings of some current privacy protection methods which may result in low interaction success rate. Simulation results show that our method can achieve effective interactions with less privacy loss.

Keyword:

interaction pattern network security network interaction privacy protection trust

Author Community:

  • [ 1 ] [Gao Feng]Beijing Univ Technol, Coll Comp Sci & Technol, Beijing 100124, Peoples R China
  • [ 2 ] [He Jingsha]Beijing Univ Technol, Sch Software Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Lv Xin]State Informat Ctr, Beijing 100045, Peoples R China
  • [ 4 ] [Zhang Feng]Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China

Reprint Author's Address:

  • [Gao Feng]Beijing Univ Technol, Coll Comp Sci & Technol, Beijing 100124, Peoples R China

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Source :

CHINA COMMUNICATIONS

ISSN: 1673-5447

Year: 2011

Issue: 4

Volume: 8

Page: 141-152

4 . 1 0 0

JCR@2022

ESI Discipline: COMPUTER SCIENCE;

JCR Journal Grade:4

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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