World Wide Web

, Volume 22, Issue 6, pp 2697–2720 | Cite as

Differential private collaborative Web services QoS prediction

  • An Liu
  • Xindi Shen
  • Zhixu Li
  • Guanfeng Liu
  • Jiajie Xu
  • Lei Zhao
  • Kai Zheng
  • Shuo ShangEmail author
Part of the following topical collections:
  1. Special Issue on Web and Big Data


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.


Web services Collaborative QoS prediction Privacy-preserving 



Research reported in this publication was partially supported Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61402313)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • An Liu
    • 1
  • Xindi Shen
    • 1
  • Zhixu Li
    • 1
  • Guanfeng Liu
    • 1
  • Jiajie Xu
    • 1
  • Lei Zhao
    • 1
  • Kai Zheng
    • 2
  • Shuo Shang
    • 3
    Email author
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduChina
  3. 3.King Abdullah University of Science and Technology (KAUST)ThuwalSaudi Arabia

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