Verifying Sequential Programs in Untrusted Environments

The lack of centralized control and regulatory oversight in an open and anonymous computing market presents significant challenges when verifying computation results. Within this environment, computing providers are highly incentivized to exploit the system by intentionally delivering incomplete or incorrect results for personal gain. Consequently, there is an urgent need to develop a robust system capable of accurately validating computation results obtained from untrusted sources. This research focuses on addressing the complex verification challenge in large-scale sequential tasks, particularly within non-deterministic processes like neural networks. To tackle this challenge, I propose a meticulous algorithm that verifies program execution integrity, ensuring precise alignment with the intended code. The proposed approach is based on the assumption of rational behavior from providers, where actions leading to negative expected utility are avoided, and task regularity, where the difficulty of each step remains independent of previous values. By addressing this fundamental problem, the proposed algorithm plays a crucial role in establishing trust in algorithmic execution within an open market framework. It offers a potential solution for accurate computation verification, especially in the context of complex sequential tasks and non-deterministic algorithms. Ultimately, this research enables the reliable utilization of computing resources in decentralized and unregulated environments, effectively meeting the critical need for trustworthy computation in such settings.