Multi Party Computation (MPC) involves partitioning a secret or task over a set of distributed compute nodes to collectively perform computational work—somewhat like BitTorrent over computation.

Secure MPC as we know it today was first developed in the early 1980s, after cryptographer Adi Shamir developed a “threshold scheme” for sharing secret data across multiple parties with some redundancy, specifically for the purpose of managing cryptographic keys. In 1982, Andrew Yao extended this approach to computation by demonstrating a mathematical proof for a “Millionaire’s Problem” where two parties could compare their wealth without revealing their actual net worths. Five years later, Goldreich et al. built upon Yao by extending fault-tolerant computation to arbitrarily many parties. Critically, the paper’s result only extended to situations where over half of the N parties, or an honest majority, are not adversarial. By proposing a solution to this toy problem for two, and eventually N party compute, Yao and Goldreich et al. respectively highlighted the core idea behind MPC: enabling computation that depends on shared information while preserving individual data privacy.

By the late 1980s, protocols like that of Ben-Or, Goldwasser, and Wigderson (BGW) formally proved the ability to compute arbitrary functions under an honest majority assumption where, as long as more than two-thirds of the participants are honest, a protocol could remain secure. As practical efficiency concerns mounted, SPDZ (pronounced speedz) was eventually created as a more viable MPC scheme for even larger majorities of dishonest actors. SPDZ introduced preprocessing techniques which allowed secure computation even with only one honest actor, provided that the malicious parties did not collude. The domain specific applications that became possible ranged from secure auctions to privacy-preserving machine learning. But despite decades of progress, MPC remains constrained by fundamental trade-offs: efficiency, trust assumptions, and coordination complexity.

In contrast to TEEs, which externalize trust to the hardware manufacturer and operator of a specific node, MPC protocols decentralize trust across multiple nodes. Much like validators for blockchains, they do so with the trust assumption that a proportion of nodes aren’t colluding or being controlled by a malicious third party. Given this last point, selecting a set of robust nodes can mean that geographically dense sets of nodes implicitly carry stronger trust assumptions—if a majority of nodes live under the same roof, an MPC network’s trust assumptions start to look similar to TEEs or a very centralized blockchain. However, an MPC network with tens of distributed nodes will encounter greater communication overheads (simply put, latency) relative to a two-party compute network.