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

Additional URL

https://doi.org/10.1109/TrustCom/BigDataSE.2018.00135

This document is currently not available here.

Share

COinS