Two-phase locality-sensitive hashing for privacy-preserving distributed service recommendation

Lianyong Qi*, Wanchun Dou, Xuyun Zhang

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

With the ever-increasing volume of services registered in various web communities, it becomes a challenging task to find the web services that a target user is really interested in from the massive candidates. In this situation, Collaborative Filtering (i.e., CF) technique is introduced to alleviate the heavy burden on the service selection decisions of target users. However, present CF-based recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios where data are multi-sourced. Furthermore, distributed service recommendation calls for the collaborations among multiple involved parties, during which the private information of users may be exposed. In view of these challenges, we propose a novel privacy-preserving distributed service recommendation approach based on two-phase Locality-Sensitive Hashing (LSH), named SerRectwo-LSH, in this paper. Concretely, in SerRectwo-LSH, we first look for the “similar friends” of a target user through a privacy-preserving two-phase LSH process; afterwards, we determine the services preferred by the “similar friends” of the target user, and then recommend them to the target user. Finally, through a set of experiments conducted on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.

Original languageEnglish
Title of host publicationCyberspace Safety and Security
Subtitle of host publication9th International Symposium, CSS 2017, Proceedings
EditorsSheng Wen, Wei Wu, Aniello Castiglione
PublisherSpringer, Springer Nature
Pages176-188
Number of pages13
ISBN (Electronic)9783319694719
ISBN (Print)9783319694702
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event9th International Symposium on Cyberspace Safety and Security, CSS 2017 - Xi'an, China
Duration: 23 Oct 201725 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10581 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Symposium on Cyberspace Safety and Security, CSS 2017
CountryChina
CityXi'an
Period23/10/1725/10/17

Keywords

  • Collaborative Filtering
  • Distributed service recommendation
  • Efficiency
  • Privacy-preservation
  • Two-Phase Locality-Sensitive hashing

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