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Amplified Locality-Sensitive Hashing for Privacy-Preserving Distributed Service Recommendation

  • Lianyong Qi
  • Wanchun Dou
  • Xuyun Zhang
  • Shui Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10656)

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 DistSR Amplify-LSH in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation in the distributed environment.

Keywords

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

Notes

Acknowledgements

This paper is partially supported by the National Key Research and Development Program of China (No. 2017YFB1001800), Natural Science Foundation of China (Nos. 61402258, 61672276), UoA Faculty Research Development Fund (No. 3714668), 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

  • Lianyong Qi
    • 1
    • 2
  • Wanchun Dou
    • 2
  • Xuyun Zhang
    • 3
  • Shui Yu
    • 4
  1. 1.School of Information Science and EngineeringQufu Normal UniversityRizhaoChina
  2. 2.State Key Laboratory for Novel Software Technology, Department of Computer Science and TechnologyNanjing UniversityNanjingChina
  3. 3.Department of Electrical and Computer EngineeringUniversity of AucklandAucklandNew Zealand
  4. 4.School of Information TechnologyDeakin UniversityMelbourneAustralia

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