Privacy-preserving recommendation system based on social relationships

Abstract

In the era of internet-based data, recommendation systems are crucial for helping users access personalized content and facilitating business promotion and sales. Recommendation systems based on social relationships have gained popularity due to their ability to use social influence and enhance the impact and credibility of recommendations. However, building a successful recommendation system faces the challenge of protecting user data privacy, as it requires a significant amount of sensitive user data. For this challenge, we propose a privacy-preserving recommendation system based on social relationships in the dual-cloud model. To achieve the secure computation of this system, we also design and improve some underlying secure protocols such as secure equality protocol and secure comparison protocol. Our underlying protocol has the advantages of high efficiency and provable security. As a result, our recommendation system can provide excellent recommendations while ensuring privacy protection. Experiments show that our system can perform secure recommendation calculations on 128-bit data with semi-honest security in approximately 5.5 s.