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DynaEgo: Privacy-Preserving Collaborative Filtering Recommender System Based on Social-Aware Differential Privacy

  • Shen Yan
  • Shiran Pan
  • Wen-Tao ZhuEmail author
  • Keke Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9977)

Abstract

Collaborative filtering plays an important role in online recommender systems, which provide personalized services to consumers by collecting and analyzing their rating histories. At the same time, such personalization may unfavorably incur privacy leakage, which has motivated the development of privacy-preserving collaborative filtering (PPCF) mechanisms. Most previous research efforts more or less impair the quality of recommendation. In this paper, we propose a social-aware algorithm called DynaEgo to improve the performance of PPCF. DynaEgo utilizes the principle of differential privacy as well as the social relationships to adaptively modify users’ rating histories to prevent exact user information from being leaked. Theoretical analysis is provided to validate our scheme. Experiments on a real data set also show that DynaEgo outperforms existent solutions in terms of both privacy protection and recommendation quality.

Keywords

Social networks Privacy preserving Recommender system Collaborative filtering Differential privacy 

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant 61272479, the National 973 Program of China under Grant 2013CB338001, and the Strategic Priority Research Program of Chinese Academy of Sciences under Grant XDA06010702.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Shen Yan
    • 1
    • 2
    • 3
  • Shiran Pan
    • 1
    • 2
    • 3
  • Wen-Tao Zhu
    • 1
    • 2
    Email author
  • Keke Chen
    • 4
  1. 1.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.Data Assurance and Communication Security Research CenterChinese Academy of SciencesBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Data Intensive Analysis and Computing (DIAC) Lab, Kno.e.sis CenterWright State UniversityDaytonUSA

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