A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation

TL;DR

This paper introduces a privacy-preserving framework for QoS-based Web service recommendation, using data obfuscation to protect user privacy while maintaining prediction accuracy, demonstrated through experiments on real-world datasets.

Contribution

It is the first to address privacy concerns in Web service QoS prediction by proposing a data obfuscation-based framework and two effective privacy-preserving prediction methods.

Findings

The proposed approaches effectively protect user privacy.

The methods maintain high QoS prediction accuracy.

Experimental results validate the framework's feasibility.

Abstract

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.