Privacy preserving user based web service recommendations

The Quality of Service (QoS)-based personalized web service recommendations have been gaining increasing popularity due to its ability to assist users in finding high quality web services. For this purpose, Collaborative Filtering (CF)-based technique has been a useful approach in that it is able to predict with high accuracy the QoS values of web services which are not invoked by the users. The basic idea behind CF-based techniques is that they identify users with similar QoS experiences and predict their QoS requirements on web services accordingly. However, as the calculation of QoS values and user similarity require parameters which may contain privacy sensitive information, users may not trust the server that provides such third-party recommendations. In general, users are usually not willing to disclose such information to a third-party as it contains their tastes and preferences as well as experiences. Therefore the main challenge is to address the need for providing accurate web service recommendations to users while preserving their privacy from any third party server, as well as to protect the privacy of individual users from one another. To tackle this challenge, we propose a new protocol for privacy preserving web service recommendation where an untrusted recommendation server is able to provide the recommendation without disclosing any private information of individual users, and with negligible loss of accuracy of QoS values. We present both privacy and experimental analysis to verify that our proposed method is secure and efficient in terms of performance.