VerifyML: Obliviously Checking Model Fairness Resilient to Malicious Model Holder

In this paper, we present
VerifyML
, the first secure inference framework to check the fairness degree of a given Machine learning (ML) model.
VerifyML
is generic and is immune to any obstruction by the malicious model holder during the verification process. We rely on secure two-party computation (2 PC) technology to implement
VerifyML
, and carefully customize a series of optimization methods to boost its performance for both linear and nonlinear layer execution. Specifically, (1)
VerifyML
allows the vast majority of overhead to be performed offline, thus meeting the low latency requirements for online inference. (2) To speed up offline preparation, we first design novel homomorphic parallel computing techniques to accelerate the authenticated Beaver's triple (including matrix- vector and convolution triples) generation procedure. It achieves up to
$1.7\times$
computation speedup and gains at least
$10.7\times$
less communication overhead compared to state-of-the-art work. (3) We also present a new cryptographic protocol to evaluate the activation functions of non-linear layers, which is
$4\times$
–
$42\times$
faster and has
$\gt 48\times$
less communication than the existing 2 PC protocol against malicious parties. In fact,
VerifyML
even beats the state-of-the-art semi-honest ML secure inference system! We provide a formal theoretical analysis for
VerifyML
security and demonstrate its performance superiority on mainstream ML models including ResNet-18 and LeNet.