Do Not Skip Over the Offline: Verifiable Silent Preprocessing From Small Security Hardware

Multi-party computation (MPC) has gained increasing attention in both research and industry, with many protocols adopting the preprocessing model to optimize online performance through the strategic use of offline-generated, data-independent correlated randomness (or correlation). However, while extensive research has been dedicated to enhancing the online phase, the equally critical offline phase remains largely overlooked. This gap imposes significant yet unaddressed challenges in both security and efficiency, hindering the practical adoption of MPC systems. To address these challenges, we build upon the pseudorandom correlation generator (PCG) concept by Boyle et al. (CRYPTO’19, FOCS’20) and propose HPCG, a programmable, verifiable, and concretely efficient PCG construction using small security hardware. Our core technique, termed verifiable silent preprocessing, enables virtually unbounded, on-demand generation of diverse correlated randomness with provable correctness while effectively reducing offline overhead in a correlation-agnostic manner. To demonstrate the benefits of our approach, we experimentally evaluate HPCG and compare it with other preprocessing techniques. We also show how HPCG can further optimize specialized secure computation tasks (e.g., shuffling and equality test) by promoting new, customized correlations, which may be of new interest.