In online social communities, many recommender systems use collaborative filtering, a
method that makes recommendations based on what are liked by other users with similar …

Privacy-preserving recommender systems have been an active research topic for many
years. However, until today, it is still a challenge to design an efficient solution without …

With the explosive growth of online social communities and massive user-generated
content, privacy-preserving recommender systems, which identify information of interest to …

Collaborative recommender systems offer a solution to the information overload problem
found in online environments such as e-commerce. The use of collaborative filtering, the …

Recommender Systems are used in various domains to generate personalized information
based on personal user data. The ability to preserve the privacy of all participants is an …

The popularity of online recommender systems has soared; they are deployed in numerous
websites and gather tremendous amounts of user data that are necessary for …

Recommender systems have become extremely common in recent years, and are applied in
a variety of domains. Existing recommender systems exhibit two major limitations:(1) Privacy …

An important security concern with traditional recommendation systems is that users
disclose information that may compromise their individual privacy when providing ratings. A …

Most existing recommender systems leverage users' complete original behavioral logs,
which are collected from mobile devices and stored by the service provider and further fed …

Collaborative filtering is a famous technique in recommendation systems. Yet, it requires the
users to reveal their preferences, which has undesirable privacy implications. Over the …