Application of Differential Privacy for Collaborative Filtering Based Recommendation System: A Survey

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

5 Citations (Scopus)

Abstract

Collaborative Filtering (CF) is the main technology in recommendation systems. It requires more users' private information to predict personal preferences, so exists the risk of privacy disclosure. Differential Privacy (DP) is a powerful privacy-preserving approach, and it has been widely applied in CF-based recommendation system. However, a comprehensive summary of DP in CF-based recommendation system is lack although many privacy-preserving CF algorithms have been proposed. This paper reviews the existing research based on the principle of DP and the application of CF algorithms. We firstly introduce the theoretical basis and the mechanism employed by DP. Then, two types of CF-based recommendation algorithms with DP are summarized including memory-based and model-based algorithms. Moreover, the optimized CF algorithms with DP in specific application fields are illustrated.

Original languageEnglish
Title of host publication2021 12th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2021
PublisherIEEE Computer Society
Pages97-101
Number of pages5
ISBN (Electronic)9781665496391
DOIs
Publication statusPublished - 2021
Event12th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2021 - Xi'an, China
Duration: 10 Dec 202112 Dec 2021

Publication series

NameProceedings - International Symposium on Parallel Architectures, Algorithms and Programming, PAAP
Volume2021-December
ISSN (Print)2168-3034
ISSN (Electronic)2168-3042

Conference

Conference12th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2021
Country/TerritoryChina
CityXi'an
Period10/12/2112/12/21

Keywords

  • Collaborative Filtering
  • Differential Privacy
  • Literature Review
  • Recommendation System
  • Survey

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