Privacy-preserving collaborative web services QoS prediction via differential privacy

Abstract

Collaborative Web services QoS prediction has become an important tool for the generation of accurate personalized QoS. 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 preserving technique, into the preprocess of QoS data prediction. We implement the proposed approach based on a general approach named Laplace mechanism and conduct extensive experiments to study its performance on a real world dataset. The experiments evaluate the privacy-accuracy trade-off on different settings and show that under some constraint, our proposed approach can achieve a better performance than baselines.