Audit and Improve Robustness of Private Neural Networks on Encrypted Data. (arXiv:2209.09996v1 [cs.LG])

Performing neural network inference on encrypted data without decryption is
one popular method to enable privacy-preserving neural networks (PNet) as a
service. Compared with regular neural networks deployed for
machine-learning-as-a-service, PNet requires additional encoding, e.g.,
quantized-precision numbers, and polynomial activation. Encrypted input also
introduces novel challenges such as adversarial robustness and security. To the
best of our knowledge, we are the first to study questions including (i)
Whether PNet is more robust against adversarial inputs than regular neural
networks? (ii) …

audit data encrypted encrypted data networks neural networks robustness