2 papers
cs.CR2021
Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation
Nuttapong Attrapadung, Koki Hamada, Dai Ikarashi +5
Privacy-preserving machine learning (PPML) aims at enabling machine learning (ML) algorithms to be used on sensitive data. We contribute to this line of research by proposing a fra…
cs.CR2018
MOBIUS: Model-Oblivious Binarized Neural Networks
Hiromasa Kitai, Jason Paul Cruz, Naoto Yanai +7
A privacy-preserving framework in which a computational resource provider receives encrypted data from a client and returns prediction results without decrypting the data, i.e., ob…