LightShark: Actively Secure Machine-Learning Inference Based on Lightweight Authenticated Distributed Comparison Function

Recently, Shark (S&P'25) considered the problem of actively twoparty secure machine learning inference using an authenticated distributed comparison function (DCF). This is the state-of-the-art work in this setting. On the other hand, Grotto (CCS'23) built a variant DCF with the key size half that of classic DCF. Unfortunately, as Shark states, it is not known how to extend Grotto to the malicious setting. In this paper, we present the first actively secure Grotto-style DCF scheme. Our authenticated DCF is deliberately designed on the correlated GGM tree and maintains the key-size advantage of semi-honest Grotto. We further implement an actively secure ML inference framework, named LightShark, which supports efficient primitives (e.g., ReLU, spline, and truncation) and ML models (e.g., VGG-16, GPT, BERT). Compared with Shark, our LightShark outperforms by 1.49× ∼ 2.69× and reduces communication costs by 66.7% for Bert-base inference. Surprisingly, for larger LLM models, the experimental evaluation demonstrates that our framework works vastly well. CCS Concepts • Theory of computation → Cryptographic protocols; • Security and privacy → Privacy-preserving protocols.