Implementing and Optimizing Matrix Triples with Homomorphic Encryption

ePrint Report: Implementing and Optimizing Matrix Triples with Homomorphic Encryption

Johannes Mono, Tim Güneysu

In today’s interconnected world, data has become a valuable asset, leading to a growing interest in protecting it through techniques such as privacy-preserving computation. Two well-known approaches are multi-party computation and homomorphic encryption with use cases such as privacy-preserving machine learning evaluating or training neural networks. For multi-party computation, one of the fundamental arithmetic operations is the secure multiplication in the malicious security model and by …

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