PUSC: Privacy-preserving user-centric skyline computation over multiple encrypted domains

• Main Authors: LIU, Ximeng, CHOO, Kim-Kwang Raymond, DENG, Robert H., YANG, Yang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Homomorphic Encryption; Multiple Encrypted Domains; Privacy-Preserving; Skyline Computation; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/4223
• Institution: Singapore Management University

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.