A Cheating Detection and Recovery Framework for Robust Multiparty Computation in the Cloud

Standard multiparty computation (MPC) protocols in dishonest-majority settings lack mechanisms to identify malicious actors or preserve computational progress, leaving them vulnerable to denial-of-service attacks in real-world deployments. Adversaries can force premature termination, resulting in wasted computational resources and significant financial losses. To address this challenge, we propose a comprehensive solution that integrates detection and recovery through three components: (1) a block-based computation model that decomposes monolithic MPC protocols into verifiable units for easy recovery; (2) lightweight cryptographic "canary values" that help detect and attribute malicious behavior without compromising privacy; and (3) a game-theoretic framework that creates economic incentives for honest protocol participation. Experimental evaluations demonstrate a reduction of up to 56.65% in recovery time compared to full restarts, with a computational overhead of 4.5%-11.8% depending on the configuration. The solution offers a practical trade-off between security and performance, thereby improving the viability of MPC in malicious cloud environments.