AuditML: An Efficient Publicly Auditable Privacy-Preserving Framework for Machine Learning Inference

AuditML is a framework that lets multiple parties run machine learning inference while keeping their data private and allowing anyone to verify the results are correct. It uses arithmetic secret sharing (a technique where data is split into random pieces distributed across parties) and separates the computation phase from the verification phase, so checking the results doesn't slow down the actual inference and can happen publicly afterward.