Computational Attestations of Polynomial Integrity Towards Verifiable Machine Learning

ePrint Report: Computational Attestations of Polynomial Integrity Towards Verifiable Machine Learning

Dustin Ray, Caroline El Jazmi

Machine-learning systems continue to advance at a rapid pace, demonstrating remarkable utility in various fields and disciplines. As these systems continue to grow in size and complexity, a nascent industry is emerging which aims to bring machine-learning-as-a-service (MLaaS) to market. Outsourcing the operation and training of these systems to powerful hardware carries numerous advantages, but challenges arise when needing to ensure privacy and the …

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