Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning

arXiv:2402.15780v1 Announce Type: new
Abstract: Recent advancements in privacy-preserving machine learning are paving the way to extend the benefits of ML to highly sensitive data that, until now, have been hard to utilize due to privacy concerns and regulatory constraints. Simultaneously, there is a growing emphasis on enhancing the transparency and accountability of machine learning, including the ability to audit ML deployments. While ML auditing and PPML have both been the subjects of intensive research, they have predominately been examined …

accountability arxiv auditing benefits constraints cs.cr data hard machine machine learning privacy privacy concerns regulatory secrets sensitive sensitive data transparency transparency and accountability