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.
|Journal||Journal of Intelligent Information Systems|
|Early online date||Aug 2018|
|Publication status||Published - 2020|
CitationLiu, 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
- Differential privacy
- Data sharing