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

Research output: Contribution to journalArticlespeer-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. Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.
Original languageEnglish
Pages (from-to)205-224
JournalJournal of Intelligent Information Systems
Early online dateAug 2018
Publication statusPublished - 2020


Liu, A., Shen, X., Xie, H., Li, Z., Liu, G., Xu, J., . . . Wang, F. L. (2020). Privacy-preserving shared collaborative web services QoS prediction. Journal of Intelligent Information Systems, 54, 205-224. doi: 10.1007/s10844-018-0525-4


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


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