Too Noisy, or Not Too Noisy? A Private Training in Machine Learning
Łukasz Krzywiecki, Grzegorz Zaborowski, Marcin Zawada · 2023
Ensuring privacy while outsourcing the training of machine learning (ML) models to cloud-based platforms is a critical concern. Although cryptographic solutions have been proposed, they often result in a substantial reduction in training accuracy and require modifications to the backend architecture. In this paper, we address the challenge of developing privacy-preserving techniques that offer adequate privacy without significantly impacting the accuracy of the ML model or the accuracy of the training process. We demonstrate that training private datasets on existing cloud-based platforms can be achieved with a high level of privacy and at a minimal cost in accuracy.