Differentially Private Collaborative Coupling Learning for Recommender Systems

Coupling learning is designed to estimate, discover, and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demand—it is desired that the collaboration should not expose either the private data of each individual owner or the model parameters trained on their datasets. In this article, we develop a distributed collaborative coupling learning system, which enables differential privacy. The proposed system defends against the adversary who has gained full knowledge of the training mechanism and the access to the model trained collaboratively. It also addresses the privacy-utility tradeoff by a provable tight sensitivity bound. Our experiments demonstrate that the proposed system guarantees favorable privacy gains at a modest cost in recommendation quality, even in scenarios with a large number of training epochs.

Authors

• Yanjun Zhang University of Technology Sydney
• Guangdong Bai
• Mingyang Zhong
• Xue Li
• Ryan K. L. Ko

Keywords

• Couplings
• Learning systems
• Differential privacy
• Training data
• Intelligent systems
• Privacy
• Recommender systems
• Collaboration
• Mathematical models
• Training Dataset
• Total Loss
• Training System
• Data Owner
• Local Dataset
• Federated Learning
• Privacy Preservation
• Loss Of Privacy
• Private Dataset
• Privacy Guarantee
• Noise Scale
• Matrix Factorization Model
• Root Mean Square Error
• Objective Function
• Random Noise
• Stochastic Gradient Descent
• Large Noise
• Parameter Server
• Coupling learning
• collaborative learning