Paper 2026/216

ECHO: Efficient Covertly-Secure Three-party Computation with Applications to Private Machine Learning

Abstract

Secure three-party computation with an honest majority is among the most efficient secure computation settings and is widely used in practice. However, achieving malicious security incurs significant overhead, often an order of magnitude higher than semi-honest protocols. Covert security provides a security–efficiency trade-off by detecting malicious behavior with a certain probability (e.g., $50\%$), deterring rational adversaries. Existing covert protocols mainly target two-party or dishonest-majority settings, with little work on efficient honest-majority three-party solutions. We present $\mathsf{ECHO}$, a family of concretely efficient protocols for covertly secure honest-majority three-party computation. We explore the design space of cheating detection and identification, and develop optimized protocols for both arithmetic and Boolean circuits, targeting different performance goals such as low latency and reduced communication. For arithmetic circuits over rings, our asymmetric-MAC-based protocol achieves an online phase only $1.26\times$ slower than the semi-honest baseline and over $5.59\times$ faster than malicious security. For Boolean circuits, our method improves over the best malicious protocol by $5\times$. We also applied $\mathsf{ECHO}$ on practical PPML tasks. $\mathsf{ECHO}$ approaches semi-honest performance while providing up to $8\times$ speedup over malicious security.