Privacy-Preserving Socialized Recommendation based on Multi-View Clustering in a Cloud Environment

TL;DR

This paper introduces a privacy-preserving socialized recommendation protocol that leverages multi-view clustering and social network data to improve recommendation quality while safeguarding user privacy in cloud environments.

Contribution

It presents a novel scheme combining multi-view clustering with privacy protection to enhance socialized recommendations in cloud settings.

Findings

The scheme effectively calculates user similarity without privacy leakage.

Experimental results demonstrate high performance and feasibility for real-world use.

The protocol protects user privacy against untrusted third parties.

Abstract

Recommendation as a service has improved the quality of our lives and plays a significant role in variant aspects. However, the preference of users may reveal some sensitive information, so that the protection of privacy is required. In this paper, we propose a privacy-preserving, socialized, recommendation protocol that introduces information collected from online social networks to enhance the quality of the recommendation. The proposed scheme can calculate the similarity between users to determine their potential relationships and interests, and it also can protect the users' privacy from leaking to an untrusted third party. The security analysis and experimental results showed that our proposed scheme provides excellent performance and is feasible for real-world applications.