TrustSVD

a popular social collaborative filtering algorithm that incorporates both of the explicit and implicit trust information

has been widely used in recommender systems. However

there is a risk of disclosure of user privacy in TrustSVD. Privacy information inference based on background knowledge is one of the great hidden dangers of user's privacy disclosure. Differential privacy has attracted much attentiaon as a privacy protection mechanism that can provide a strict theoretical guarantee for protection objects. In this article

we propose DPTrustSVD

a novel collaborative filtering algorithm that applies Differential privacy to TrustSVD and has the ability of privacy preserving. Theoretical analysis and experimental results show that DPTrustSVD not only provides a strict theoretical guarantee for users' privacy information

but also maintains a high prediction accuracy.