A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation
Refereed conference paper presented and published in conference proceedings


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AbstractQoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To facilitate such recommendations, existing studies suggest the use of collaborative filtering techniques for personalized QoS prediction. These approaches, by leveraging partially observed QoS values from users, can achieve high accuracy of QoS predictions on the unobserved ones. However, the requirement to collect users' QoS data likely puts user privacy at risk, thus making them unwilling to contribute their usage data to a Web service recommender system. As a result, privacy becomes a critical challenge in developing practical Web service recommender systems. In this paper, we make the first attempt to cope with the privacy concerns for Web service recommendation. Specifically, we propose a simple yet effective privacy-preserving framework by applying data obfuscation techniques, and further develop two representative privacy-preserving QoS prediction approaches under this framework. Evaluation results from a publicly-available QoS dataset of real-world Web services demonstrate the feasibility and effectiveness of our privacy-preserving QoS prediction approaches. We believe our work can serve as a good starting point to inspire more research efforts on privacy-preserving Web service recommendation.
All Author(s) ListZhu J., He P., Zheng Z., Lyu M.R.
Name of ConferenceIEEE International Conference on Web Services, ICWS 2015
Start Date of Conference27/06/2015
End Date of Conference02/07/2015
Place of ConferenceNew York
Country/Region of ConferenceUnited States of America
Year2015
Month8
Day13
Pages241 - 248
ISBN9781467380904
LanguagesEnglish-United Kingdom
Keywordscollaborative filtering, privacy preservation, QoS prediction, Web service recommendation

Last updated on 2020-27-05 at 02:13