Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …

Traditional recommendation systems require users to upload local data to the server to
generate recommendation results. In this process, users' privacy is easy to disclose …

With increasing concern over data privacy, split learning has become a widely used
distributed machine learning paradigm in practice, where two participants (namely the non …

Federated recommendation systems (FRecSys) alleviate the privacy issues of
recommendation systems (RecSys) by distributing model training tasks onto users' local …

Recommendation systems powered by AI are widely used to improve user experience.
However, it inevitably raises privacy leakage and other security issues due to the utilization …

Recommender systems process abundances of user data to generate recommendations
that fit well to each individual user. This utilization of user data can pose severe threats to …

Biometric patterns have been used for authentication purposes for more than decades.
Although traditional biometric measurements including fingerprints, facial recognition, and …

Abstract Knowledge Graphs (KGs) have been instrumental in mitigating the challenges of
cold start and data scarcity in recommender systems, serving as a kind of auxiliary …