Differential private collaborative Web services QoS prediction

An Liu, Xindi Shen, Zhixu Li, Guanfeng Liu, Jiajie Xu, Lei Zhao, Kai Zheng, Shuo Shang

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Collaborative Web services QoS prediction has proved to be an important tool to estimate accurately personalized QoS experienced by individual users, which is beneficial for a variety of operations in the service ecosystem, such as service selection, composition and recommendation. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We introduce differential privacy, a rigorous and provable privacy model, into the process of collaborative QoS prediction. We first present DPS, a method that disguises a user’s observed QoS values by applying differential privacy to the user’s QoS data directly. We show how to integrate DPS with two representative collaborative QoS prediction approaches. To improve the utility of the disguised QoS data, we present DPA, another QoS disguising method which first aggregates a user’s QoS data before adding noise to achieve differential privacy. We evaluate the proposed methods by conducting extensive experiments on a real world Web services QoS dataset. Experimental results show our approach is feasible in practice.
Original languageEnglish (US)
Pages (from-to)2697-2720
Number of pages24
JournalWorld Wide Web
Issue number6
StatePublished - Apr 4 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Research reported in this publication was partially supported Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61402313)


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