Abstract
In high-stakes multi-party policy making based on machine learning and simulation models involving independent computing agents, a notion of trust in results is critical in facilitating transparency, accountability, and collaboration. Using a novel combination of distributed validation of atomic computation blocks and a blockchain-based immutable audit mechanism, this work proposes a framework for distributed trust in computations. In particular we address the scalability problem by reducing the storage and communication costs using a lossy compression scheme. This framework guarantees not only verifiability of final results, but also the validity of local computations, and its cost-benefit tradeoffs are studied using a synthetic example of training a neural network.
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