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Many systems today distribute trust across multiple parties such that the system provides certain security properties if a subset of the parties are honest. In the past few years, we have seen an explosion of academic and industrial cryptographic systems built on distributed trust, including secure multi-party computation applications (e.g., private analytics, secure learning, and private key recovery) and blockchains. These systems have great potential for improving security and privacy, but face a significant hurdle on the path to deployment. We initiate study of the following problem: a single organization is, by definition, a single party, and so how can a single organization build a distributed-trust system where corruptions are independent? We instead consider an alternative formulation of the problem: rather than ensuring that a distributed-trust system is set up correctly by design, what if instead, users can audit a distributed-trust deployment? We propose a framework that enables a developer to efficiently and cheaply set up any distributed-trust system in a publicly auditable way. To do this, we identify two application-independent building blocks that we can use to bootstrap arbitrary distributed-trust applications: secure hardware and an append-only log. We show how to leverage existing implementations of these building blocks to deploy distributed-trust systems, and we give recommendations for infrastructure changes that would make it easier to deploy distributed-trust systems in the future. 

The last decade has seen an explosion in the number of new secure multi-party computation (MPC) protocols that enable collaborative computation on sensitive data. No single MPC protocol is optimal for all types of computation. As a result, researchers have created hybrid-protocol compilers that translate a program into a hybrid protocol that mixes different MPC protocols. Hybrid-protocol compilers crucially rely on accurate cost models, which are handwritten by the compilers' developers, to choose the correct schedule of protocols. In this paper, we propose CostCO, the first automatic MPC cost modeling framework. CostCO develops a novel API to interface with a variety of MPC protocols, and leverages domain-specific properties of MPC in order to enable efficient and automatic cost-model generation for a wide range of MPC protocols. CostCO employs a two-phase experiment design to efficiently synthesize cost models of the MPC protocol's runtime as well as its memory and network usage. We verify CostCO's modeling accuracy for several full circuits, characterize the engineering effort required to port existing MPC protocols, and demonstrate how hybrid-protocol compilers can leverage CostCO's cost models.