Amplified locality-sensitive hashing for privacy-preserving distributed service recommendation

Lianyong Qi*, Wanchun Dou, Xuyun Zhang, Shui Yu

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

With the ever-increasing volume of services registered in various web communities, service recommendation techniques, e.g., Collaborative Filtering (i.e., CF) have provided a promising way to alleviate the heavy burden on the service selection decisions of target users. However, traditional CF-based service recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios as well as the resulted privacy leakage risks. In view of this shortcoming, Locality-Sensitive Hashing (LSH) technique is recruited in this paper to protect the private information of users when distributed service recommendations are made. Furthermore, LSH is essentially a probability-based search technique and hence may generate “False-positive” or “False-negative” recommended results; therefore, we amplify LSH by AND/OR operations to improve the recommendation accuracy. Finally, through a set of experiments deployed on a real distributed service quality dataset, i.e., WS-DREAM, we validate the feasibility of our proposed recommendation approach named DistSRAmplify-LSH in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation in the distributed environment.

Original languageEnglish
Title of host publicationSecurity, Privacy, and Anonymity in Computation, Communication, and Storage - 10th International Conference, SpaCCS 2017, Proceedings
Subtitle of host publication10th International Conference, SpaCCS 2017, Guangzhou, China, December 12-15, 2017, Proceedings
EditorsGuojun Wang, Mohammed Atiquzzaman, Zheng Yan, Kim-Kwang Raymond Choo
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages280-297
Number of pages18
ISBN (Electronic)9783319723891
ISBN (Print)9783319723884
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017 - Guangzhou, China
Duration: 12 Dec 201715 Dec 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10656 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017
CountryChina
CityGuangzhou
Period12/12/1715/12/17

Keywords

  • Amplified Locality-Sensitive Hashing
  • Collaborative Filtering
  • Distributed service recommendation
  • Privacy-preservation
  • Recommendation accuracy

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