A Framework of Privacy Preserving QOS Prediction used for Web Service Recommendation

With increasing existence and reception of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming essential for unfolding nonfunctional characteristics of Web services. Collaborative filtering (CF) techniques are becoming progressively popular with the progress of the Internet. To demeanor collaborative filtering, data from customers are needed. Though, gathering high quality data from customers is a difficult task because many customers are so concerned about their privacy that they might choose to give fabricated information. We propose a randomized disconcertion technique to safeguard users privacy while still constructing accurate recommendations. Then, based on the collected QoS data, a collaborative filtering approach is designed to calculate Web service QoS.