Can I read Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation on EtoBox?

Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation by Attrapadung, Nuttapong; Hamada, Koki; Ikarashi, Dai; Kikuchi, Ryo; Matsuda, Takahiro; Mishina, Ibuki; Morita, Hiraku; Schuldt, Jacob C. N. is a scholarly article available to read on EtoBox.

What is Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation about?

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 framework that allows efficient and secure evaluation of full-fledged state-of-the-art ML algorithms via secure multi-party computation (MPC). This is in contrast to most prior works, which substitute ML algorithms with approximated "MPC-friendly" variants. A drawback of the latter approach is that fine-tuning of the combined ML and MPC algorithms is required, which might lead to less efficient algorithms or inferior quality ML. This is an issue for secure deep neural networks (DNN) training in particular, as this involves arithmetic algorithms thought to be "MPC-unfriendly", namely, integer division, exponentiation, inversion, and square root. In this work, we propose secure and efficient protocols for the above seemingly MPC-unfriendly computations. Our protocols are three-party protocols in the honest-majority setting, and we propose both passively secure and actively secure with abort variants. A notable feature of our protocols is that they simultaneously provide high accuracy and efficiency. This frame

Author

Attrapadung, Nuttapong; Hamada, Koki; Ikarashi, Dai; Kikuchi, Ryo; Matsuda, Takahiro; Mishina, Ibuki; Morita, Hiraku; Schuldt, Jacob C. N.

Published

2021

Language

EN