Darl: Dynamic Parameter Adjustment For Lwe-Based Secure Inference

Packed additive homomorphic encryption (PAHE) based secure neural network inference is attracting increasing attention in the field of applied cryptography. In this work, we seek to improve the practicality of LWE-based secure inference by dynamically changing the cryptographic parameters depending on the underlying architecture of the neural network. First, we develop and apply theoretical methods to closely examine the error behavior of secure inference, and propose parameters that can reduce as much as 67% of ciphertext size when smaller networks are used. Second, we use rare-event simulation techniques based on the sigma-scale sampling method to provide tight bounds on the size of cumulative errors drawn from (somewhat) arbitrary distributions. Finally, in the experiment, we instantiate an example PAHE scheme and show that we can further reduce the ciphertext size by 3.3x if we adopt a binarized neural network architecture, along with a computation speedup of 2x-3x.