Secure Federated Matrix Factorization

To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user’s personal raw private data. In this article, we propose a secure matrix factorization framework under the federated learning setting, called FedMF. First, we design a user-level distributed matrix factorization framework where the model can be learned when each user only uploads the gradient information (instead of the raw preference data) to the server. While gradient information seems secure, we prove that it could still leak users’ raw data. To this end, we enhance the distributed matrix factorization framework with homomorphic encryption. We implement the prototype of FedMF and test it with a real movie rating dataset. Results verify the feasibility of FedMF. We also discuss the challenges for applying FedMF in practice for future research.

Authors

• Di Chai
• Leye Wang
• Kai Chen Shanghai Key Lab of Intell. Info. Processing, School of Computer Science, Fudan University, Shanghai Collaborative Innovation Center on Intelligent Visual Computing
• Qiang Yang Hong Kong University of Science and Technology, Hong Kong SAR, China

Keywords

• Servers
• Encryption
• Privacy
• Data models
• Mathematical model
• Machine learning
• Matrix Factorization
• Raw Data
• Personal Data
• Data Privacy
• Collusion
• Types Of Users
• Privacy Protection
• Information Leakage
• Recommender Systems
• User Privacy
• Gradient Information
• Federated Learning
• Sensitive Attributes
• Encryption Scheme
• Differential Privacy
• Root Mean Square Error
• Feature Space
• Stochastic Gradient Descent
• Public Key
• Secret Key
• Secure Channel
• Max Number
• Profile Matrix
• User Profile
• Definition Of Security
• Decline In Accuracy
• Real-world Implementation
• Decryption Process
• IEEE Intelligent system
• Security and Privacy Protection
• Distributed system