Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations

Authors: Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt
Published: 2022-06-13

Source: https://arxiv.org/abs/2206.05893

PDF: https://arxiv.org/pdf/2206.05893

Labels Predicted by AI

深層学習手法 敵対的攻撃手法 トレーニングプロトコル

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

Abstract

Due to the computational cost of running inference for a neural network, the need to deploy the inferential steps on a third party’s compute environment or hardware is common. If the third party is not fully trusted, it is desirable to obfuscate the nature of the inputs and outputs, so that the third party can not easily determine what specific task is being performed. Provably secure protocols for leveraging an untrusted party exist but are too computational demanding to run in practice. We instead explore a different strategy of fast, heuristic security that we call Connectionist Symbolic Pseudo Secrets. By leveraging Holographic Reduced Representations (HRR), we create a neural network with a pseudo-encryption style defense that empirically shows robustness to attack, even under threat models that unrealistically favor the adversary.