Fast and Energy-efficient Distributed Consensus for Blockchains

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Multi-party computation as a service for privacy-preserving distributed applications

Publication . Carvalho, Miguel João Novo Faísca de; Ferreira, Bernardo Luís da Silva; Bessani, Alysson Neves

Multi-Party Computation (MPC) has recently gained interest as a tool to perform secure, distributed computations. However, current work presents limitations that hinder their practical application, namely not supporting a framework suited for long computations, assuming a fixed participant size, not tolerating faults, and running in strictly synchronous environments. We present MPCServe, a new practical Multi-Party Computation framework that dynamically performs computations while adopting a MPC-as-a-Service model for ease of usage. Our framework allows lightweight clients to outsource their privacy-preserving computations on encrypted data to a set of untrusted servers while guaranteeing computational output in the presence of t Byzantine faults assuming a total of at least n > 3t servers. MPCServe extends COBRA, a confidential Byzantine Fault Tolerance State Machine Replication framework that uses Dynamic Proactive Secret Sharing (DPSS) for storing data with high levels of privacy, integrity, and availability. Leveraging its faulttolerance guarantees and the homomorphic properties of DPSS, MPCServe builds a fluid-style, maliciously secure MPC infrastructure for asynchronous networks that allows servers to join and leave during the computational effort.

2025Master thesis

Open access