  QoS-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.
