PUSC: Privacy-preserving user-centric skyline computation over multiple encrypted domains
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
Discipline
Information Security
Research Areas
Cybersecurity
Publication
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
First Page
958
Last Page
963
ISBN
9781538643884
Identifier
10.1109/TrustCom/BigDataSE.2018.00135
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
LIU, Ximeng; CHOO, Kim-Kwang Raymond; DENG, Robert H.; and YANG, Yang.
PUSC: Privacy-preserving user-centric skyline computation over multiple encrypted domains. (2018). 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. 958-963.
Available at: https://ink.library.smu.edu.sg/sis_research/4223
Additional URL
https://doi.org/10.1109/TrustCom/BigDataSE.2018.00135