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Homomorphic encryption

CROSS — Leveraging AI ASICs for Homomorphic Encryption

CROSS — Leveraging AI ASICs for Homomorphic Encryption

The talk presents CROSS and Morph, two compiler frameworks that enable existing AI accelerators, like Google's TPUs, to efficiently execute cryptographic workloads. CROSS focuses on Homomorphic Encryption (HE) and Morph on Zero-Knowledge Proofs (ZKP), demonstrating how to transform high-precision modular arithmetic into low-precision matrix operations that TPUs excel at, thereby achieving state-of-the-art performance and energy efficiency without any hardware modifications.

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.