Privacy-preserving Computation over Encrypted Vectors

Rui Hu, Wenxiu Ding*, Zheng Yan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

Cloud computing allows users to outsource massive amounts of data to a cloud server for storage and analysis, which breaks the bottleneck of limited local resources. However, it makes user data exposed and possibly be accessed by unauthorized entities. Owing to privacy concern, users are inclined to upload encrypted data to a cloud server, but encryption limits operations over original data and affects access to a processing result. Though lots of schemes have been proposed to achieve some basic operations over encrypted data, it still lacks the research on the dot product of encrypted vectors. In this paper, we propose two privacy-preserving dot product schemes based on a dual server model, which can flexibly support single-user access and multiuser access to a final data processing result. Furthermore, we extend them to achieve privacy-preserving Support Vector Machine (SVM) prediction algorithm. Finally, we give security analysis of our proposed schemes and demonstrate their availability and practicality through simulation and comparison with existing works.
Original languageEnglish
Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-8298-8
DOIs
Publication statusPublished - Dec 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Global Communications Conference - Taipei, Taiwan, Republic of China
Duration: 7 Dec 202011 Dec 2020

Publication series

NameIEEE Global Communications Conference
ISSN (Print)2334-0983
ISSN (Electronic)2576-6813

Conference

ConferenceIEEE Global Communications Conference
Abbreviated titleGLOBECOM
Country/TerritoryTaiwan, Republic of China
CityTaipei
Period07/12/202011/12/2020

Keywords

  • cloud computing
  • privacy preserving
  • encrypted vector
  • dot product
  • access control
  • SVM

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