Privacy-preserving shared collaborative web services QoS prediction

An Liu, Xindi Shen, Haoran Xie*, Zhixu Li, Guanfeng Liu, Jiajie Xu, Lei Zhao, Fu Lee Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Collaborative Web services QoS prediction (CQoSP) has been proved to be an effective tool to predict unknown QoS values of services. Recently a number of efforts have been made in this area, focusing on improving the accuracy of prediction. In this paper, we consider a novel kind of CQoSP, shared CQoSP, where multiple parties share their data with each other in order to provide more accurate prediction than a single party could do. To encourage data sharing, we propose a privacy-preserving framework which enables shared collaborative QoS prediction without leaking the private information of the involved party. Our framework is based on differential privacy, a rigorous and provable privacy model. We conduct extensive experiments on a real Web services QoS dataset. Experimental results show the proposed framework increases prediction accuracy while ensuring the privacy of data owners.

Original languageEnglish
Pages (from-to)205-224
Number of pages20
JournalJournal of Intelligent Information Systems
Volume54
Issue number1
DOIs
Publication statusPublished - Feb 2020
Externally publishedYes

Keywords

  • Collaborative QoS prediction
  • Data sharing
  • Differential privacy
  • Privacy-preserving

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