A secure and efficient framework for privacy preserving social recommendation

Shushu Liu, An Liu, Guanfeng Liu, Zhixu Li, Jiajie Xu, Pengpeng Zhao, Lei Zhao

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

6 Citations (Scopus)

Abstract

The well-known cold start problem in traditional collaborative filtering based recommender systems can be effectively addressed by social recommendation, which has been witnessed by a number of researches recently in many application domains. The social graph used in social recommendation is typically owned by a third party such as Facebook and Twitter, and should be hidden from recommender systems for obvious reasons of commercial benefits, as well as due to privacy legislation. In this paper, we present a secure and efficient framework for privacy preserving social recommendation. Our framework is built on mature cryptographic building blocks, including Paillier cryptosystem and Yao’ protocol, which lays a solid foundation for the security of our framework. Using our framework, the owner of sales data and the owner of social graph can cooperatively compute social recommendation, without revealing their private data to each other. We theoretically prove the security and analyze the complexity of our framework. Empirical study shows our framework has a linear complexity with respect to the number of users and items in recommender systems and is practical in real applications.

Original languageEnglish
Title of host publicationWeb Technologies and Applications
Subtitle of host publication17th Asia-Pacific Web Conference, APWeb 2015, Proceedings
EditorsReynold Cheng, Bin Cui, Zhenjie Zhang, Ruichu Cai, Jia Xu
PublisherSpringer, Springer Nature
Pages781-792
Number of pages12
Volume9313
ISBN (Electronic)9783319252551
ISBN (Print)9783319252544
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventAsia-Pacific Web Conference (17th : 2015) - Guangzhou, China
Duration: 18 Sep 201520 Sep 2015
Conference number: 17th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9313
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

ConferenceAsia-Pacific Web Conference (17th : 2015)
Abbreviated titleAPWeb 2015
CountryChina
CityGuangzhou
Period18/09/1520/09/15

Keywords

  • Privacy preserving
  • Recommender system
  • Secure two-party computation
  • Social recommendation

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