Paper 2023/1684

Nomadic: Normalising Maliciously-Secure Distance with Cosine Similarity for Two-Party Biometric Authentication

Abstract

Computing the distance between two non-normalized vectors $\mathbfit{x}$ and $\mathbfit{y}$, represented by $\Delta(\mathbfit{x},\mathbfit{y})$ and comparing it to a predefined public threshold $\tau$ is an essential functionality used in privacy-sensitive applications such as biometric authentication, identification, machine learning algorithms ({\em e.g.,} linear regression, k-nearest neighbors, etc.), and typo-tolerant password-based authentication. Tackling a widely used distance metric, {\sc Nomadic} studies the privacy-preserving evaluation of cosine similarity in a two-party (2PC) distributed setting. We illustrate this setting in a scenario where a client uses biometrics to authenticate to a service provider, outsourcing the distance calculation to two computing servers. In this setting, we propose two novel 2PC protocols to evaluate the normalising cosine similarity between non-normalised two vectors followed by comparison to a public threshold, one in the semi-honest and one in the malicious setting. Our protocols combine additive secret sharing with function secret sharing, saving one communication round by employing a new building block to compute the composition of a function $f$ yielding a binary result with a subsequent binary gate. Overall, our protocols outperform all prior works, requiring only two communication rounds under a strong threat model that also deals with malicious inputs via normalisation. We evaluate our protocols in the setting of biometric authentication using voice, and the obtained results reveal a notable efficiency improvement compared to existing state-of-the-art works.