Two-Phase Locality-Sensitive Hashing for Privacy-Preserving Distributed Service Recommendation

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10581)

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 SerRec two-LSH , in this paper. Concretely, in SerRec two-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.

Keywords

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

Acknowledgement

This paper is partially supported by Natural Science Foundation of China (No. 61402258, 61672276), key Research and Development Project of Jiangsu Province (No. BE2015154, No. BE2016120), Open Project of State Key Laboratory for Novel Software Technology (No. KFKT2016B22).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information Science and EngineeringQufu Normal UniversityJiningChina
  2. 2.Chinese Academy of Education Big DataQufu Normal UniversityJiningChina
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  4. 4.Department of Electrical and Computer EngineeringUniversity of AucklandAucklandNew Zealand

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