A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption

Federated Learning FL enables collaborative model training without sharing raw data, making it a promising approach for privacy-sensitive domains. Despite its potential, FL faces significant challenges, particularly in terms of communication overhead and data privacy. Privacy-preserving Technique...

Communication-Efficient Publication of Sparse Vectors under Differential Privacy

Whitepaper called Communication-Efficient Publication Of Sparse Vectors Under Differential Privacy...

Standing Firm in 5G: a Single-Round, Dropout-Resilient Secure Aggregation for Federated Learning

Federated learning FL is well-suited to 5G networks, where many mobile devices generate sensitive edge data. Secure aggregation protocols enhance privacy in FL by ensuring that individual user updates reveal no information about the underlying client data. However, the dynamic and large-scale...

Sparsification under Siege: Defending against Poisoning Attacks in Communication-Efficient Federated Learning

Federated Learning FL enables collaborative model training across distributed clients while preserving data privacy, yet it faces significant challenges in communication efficiency and vulnerability to poisoning attacks. While sparsification techniques mitigate communication overhead by...

Logic Flaw Vulnerability in MSS Streaming Media Server at Suzhou Kodak Technology Co.

Ltd. is a leading provider of video and security products and solutions, committed to video conferencing, video surveillance and a wealth of video application solutions to help all kinds of government and enterprise customers to improve communication and management efficiency. A logic flaw exists...