Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
Wenhan Dong, Chao Lin, Xinlei He, Xinyi Huang, Shengmin Xu·December 02, 2024
Summary
Homomorphic Adversarial Networks (HANs) address privacy-preserving federated learning (PPFL) challenges, offering robust security against attacks. HANs use neural networks with an Aggregatable Hybrid Encryption scheme, solving key distribution and decryption issues. Compared to non-private federated learning, HANs show minimal accuracy loss (at most 1.35%) and increase encryption aggregation speed by 6,075 times with 29.2× more communication overhead. Contributions include pioneering neural networks for privacy protection in cryptography and FL protocols, demonstrating practical viability across multiple datasets.
Introduction
Background
Overview of privacy-preserving federated learning (PPFL)
Challenges in PPFL, including data privacy and security
Importance of homomorphic encryption in addressing these challenges
Objective
To introduce and explain the concept of Homomorphic Adversarial Networks (HANs)
To highlight the benefits of HANs in enhancing privacy in federated learning
To discuss the practical implications and contributions of HANs in cryptography and federated learning protocols
Method
Data Collection
Sources of data for training HANs
Techniques for collecting data while maintaining privacy
Data Preprocessing
Methods for preparing data for use in HANs
Importance of data preprocessing in enhancing model performance and privacy
Homomorphic Adversarial Networks (HANs)
Architecture
Overview of the HAN architecture
Components and their roles in the network
Aggregatable Hybrid Encryption
Explanation of the encryption scheme used in HANs
How it facilitates secure and efficient data aggregation
Security Against Attacks
Discussion on how HANs protect against various types of attacks
Analysis of the robustness of HANs in adversarial environments
Performance Evaluation
Accuracy Loss
Comparison of accuracy between HANs and non-private federated learning
Quantification of the maximum accuracy loss (1.35%)
Encryption Aggregation Speed
Improvement in encryption aggregation speed (6,075 times faster)
Analysis of the trade-off with increased communication overhead (29.2× more)
Communication Overhead
Detailed examination of the additional communication costs
Discussion on the practical implications of increased overhead
Contributions
Neural Networks for Privacy Protection
Pioneering use of neural networks in cryptography
Significance in enhancing privacy in federated learning
Practical Viability
Demonstration of HANs across multiple datasets
Real-world applicability and scalability of HANs in PPFL
Conclusion
Summary of Key Findings
Recap of the main contributions and benefits of HANs
Future Directions
Potential areas for further research and development
Outlook on the future of privacy-preserving federated learning
Basic info
papers
cryptography and security
machine learning
artificial intelligence
Advanced features
Insights
What are the key contributions of HANs in the field of cryptography and federated learning protocols, and how do they demonstrate practical viability across multiple datasets?
In comparison to non-private federated learning, what are the accuracy loss and encryption aggregation speed improvements demonstrated by HANs?
How do HANs utilize an Aggregatable Hybrid Encryption scheme to address key distribution and decryption issues in federated learning?