Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda
D Pramod - Data Technologies and Applications, 2022 - emerald.com
Purpose This study explores privacy challenges in recommender systems (RSs) and how
they have leveraged privacy-preserving technology for risk mitigation. The study also …
they have leveraged privacy-preserving technology for risk mitigation. The study also …
Experimenting with agent-based model simulation tools
Agent-based models (ABMs) are one of the most effective and successful methods for
analyzing real-world complex systems by investigating how modeling interactions on the …
analyzing real-world complex systems by investigating how modeling interactions on the …
A survey on trustworthy recommender systems
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …
deployed in almost every corner of the web and facilitate the human decision-making …
Efficient federated item similarity model for privacy-preserving recommendation
X Ding, G Li, L Yuan, L Zhang, Q Rong - Information Processing & …, 2023 - Elsevier
Previous federated recommender systems are based on traditional matrix factorization,
which can improve personalized service but are vulnerable to gradient inference attacks …
which can improve personalized service but are vulnerable to gradient inference attacks …
Recent advances and future challenges in federated recommender systems
Recommender systems are an integral part of modern-day user experience. They
understand their preferences and support them in discovering meaningful content by …
understand their preferences and support them in discovering meaningful content by …
Discrete Federated Multi-behavior Recommendation for Privacy-Preserving Heterogeneous One-Class Collaborative Filtering
Recently, federated recommendation has become a research hotspot mainly because of
users' awareness of privacy in data. As a recent and important recommendation problem, in …
users' awareness of privacy in data. As a recent and important recommendation problem, in …
Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and Blockchain
Recommender systems (RS) play an integral role in many online platforms. Exponential
growth and potential commercial interests are raising significant concerns around privacy …
growth and potential commercial interests are raising significant concerns around privacy …
Privacy-preserving graph convolution network for federated item recommendation
In traditional recommender systems, we often build models based on a centralized storage
of user data, which however will lead to user privacy concerns and risks. In this paper, we …
of user data, which however will lead to user privacy concerns and risks. In this paper, we …
HN3S: A federated autoencoder framework for collaborative filtering via hybrid negative sampling and secret sharing
L Zhang, G Li, L Yuan, X Ding, Q Rong - Information Processing & …, 2024 - Elsevier
Federated recommender systems can serve users with suitable item recommendations
while preserving their privacy, but most current works cannot serve non-participant users …
while preserving their privacy, but most current works cannot serve non-participant users …
Horizontal Federated Recommender System: A Survey
Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …
privacy leakage exists in the centralized-training recommender system (RecSys). To this …