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PUSC: Privacy-preserving user-centric skyline computation over multiple encrypted domains
2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications / 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE): New York, August 1-3: Proceedings
  • Ximeng LIU
  • Kim-Kwang Raymond CHOO
  • Robert H. DENG, Singapore Management University
  • Yang YANG
Publication Type
Conference Proceeding Article
Publication Date
8-2018
Abstract

In this paper, we present a new privacy-preserving user-centric skyline computation framework over different encrypted domains, which we referred to as PUSC. With PUSC, a user can flexibly obtain the skyline set from different service providers without disclosing user preferences to third parties in the system. Specifically, we introduce a secure user-defined vector dominance protocol to compare the vector dominance relationship between two encrypted vectors, according to user's preference. This serves as the core protocol in PUSC. Detailed security analysis shows that the proposed PUSC achieves the goal of selecting skyline set according to authorized users' preferences without leaking their privacy to other parties. In addition, performance evaluation demonstrates PUSC's efficiency in terms of providing skyline computation and transmission while minimizing privacy disclosure.

Keywords
  • Homomorphic Encryption,
  • Multiple Encrypted Domains,
  • Privacy-Preserving,
  • Skyline Computation
ISBN
9781538643884
Identifier
10.1109/TrustCom/BigDataSE.2018.00135
Publisher
IEEE
City or Country
Piscataway, NJ
Additional URL
https://doi.org/10.1109/TrustCom/BigDataSE.2018.00135
Citation Information
Ximeng LIU, Kim-Kwang Raymond CHOO, Robert H. DENG and Yang YANG. "PUSC: Privacy-preserving user-centric skyline computation over multiple encrypted domains" 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications / 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE): New York, August 1-3: Proceedings (2018) p. 958 - 963
Available at: http://works.bepress.com/robert-deng/310/