Title: Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach,
Abstract: Large-scale computational experiments, often running over weeks and over
large datasets, are used extensively in fields such as epidemiology,
meteorology, computational biology, and healthcare to understand phenomena, and
design high-stakes policies affecting everyday health and economy. For
instance, the OpenMalaria framework is a computationally-intensive simulation
used by various non-governmental and governmental agencies to understand
malarial disease spread and effectiveness of intervention strategies, and
subsequently design healthcare policies. Given that such shared results form
the basis of inferences drawn, technological solutions designed, and day-to-day
policies drafted, it is essential that the computations are validated and
trusted. In particular, in a multi-agent environment involving several
independent computing agents, a notion of trust in results generated by peers
is critical in facilitating transparency, accountability, and collaboration.
Using a novel combination of distributed validation of atomic computation
blocks and a blockchain-based immutable audits mechanism, this work proposes a
universal framework for distributed trust in computations. In particular we
address the scalaibility 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.