Homomorphic encryption

Efficient Secure Aggregation for Federated Learning

Efficient Secure Aggregation for Federated Learning

Varun Madathil from Yale University presents Tacita, a novel, single-server protocol for secure aggregation in Federated Learning (FL). Tacita is designed to address the unique constraints of the FL environment, such as client dropouts and the absence of client-to-client communication. The protocol achieves one-shot execution with constant-size communication and robustness against dropouts by introducing two new cryptographic primitives: succinct multi-key linearly homomorphic threshold signatures (MKLHTS) and a homomorphic variant of Silent Threshold Encryption.

Encrypted Computation: What if Decryption Wasn’t Needed? • Katharine Jarmul • GOTO 2024

Encrypted Computation: What if Decryption Wasn’t Needed? • Katharine Jarmul • GOTO 2024

An exploration of encrypted computation, detailing how techniques like homomorphic encryption and multi-party computation can enable machine learning on encrypted data. The summary covers the core mathematical principles, real-world use cases, and open-source libraries to build more private and trustworthy AI systems.