TACEO and Aztec Foundation are teaming up to create a Private Shared State, an encrypted environment that supports updates, multi-computation and auditing under one private decentralized roof.
Summary
- TACEO and Aztec Foundation are partnering to bring Private Shared State into Ethereum.
- PSS differs from existing MPC solutions by allowing shared, persistent private states on-chain, with a focus on developer usability through TACEO’s coNoir toolkit.
- TACEO claims its system is built with post-quantum security in mind, using information-theoretically secure protocols and exploring hash-based proof systems.
TACEO, the company behind Worldcoin’s encrypted iris-scanning network and the largest known multiparty computation database, has partnered with the Aztec Foundation, nonprofit organization that supports the Aztec Network, to create a Private Shared State on Ethereum.
The partnership claims it would allow multiple parties to verify blockchain transactions and contracts, without exposing the underlying information or relying on a centralized entity to verify them. It combines TACEO’s collaborative computation abilities with Aztec’s privacy-first Layer 2 on Ethereum (ETH).
TACEO CEO Lukas Helminger, tells crypto.news that the PSS serves to extend the capabilities of multiparty computation or MPC to new areas that it was previously limited to. The system will enable multiple users to collaborate on encrypted data sets over which computation is done.
“In brief, PSS lets multiple parties jointly maintain and compute over a single, shared piece of private state, and then commit that state on-chain with a proof that’s publicly verifiable,” said Helminger.
Through the collaboration, Aztec developers will be able to use enhanced tooling that supports complex and collaborative computing. Developers will be able to perform general-purpose computation on encrypted data from different sources, yielding functionality and privacy beyond what web2 is capable of.
The PSS is poised to facilitate a range of different use cases, including trustless financial markets, collaborative AI model training
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Author: Trisha Husada
