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: Journal PublicationsJournal Article (refereed)peer-review

4 Citations (Scopus)


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
Issue number1
Early online date20 Aug 2018
Publication statusPublished - Feb 2020
Externally publishedYes

Bibliographical note

Research reported in this publication was partially supported Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61402313). Haoran Xie’s work of this study has been supported by the Funding Support to ECS Proposal (RG 23/2017-2018R), Seed Fund for General Research Fund / Early Career Scheme (SFG-6) of 2018 Dean’s Research Fund to MIT Department and Individual Research Scheme of Dean’s Research Fund (IRS-8) of The Education University of Hong Kong. Fu Lee Wang’s work has been supported by a grant from Research Grants Council of Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16).


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


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