@MinaProtocol is propelling a new wave of AI systems through its MinaZKML library. You might ask yourself, "Why should a zero-knowledge-powered blockchain get involved in the AI industry?" But the right question is,
"Why shouldn't a zero-knowledge-powered blockchain be involved in the AI industry?" The rapid development in the AI space has kept both optimists and pessimists on their toes. With this in mind, it is essential to build AI systems and applications that are cryptographically guaranteed, which is the main idea behind the MinaZKML library.
"Why shouldn't a zero-knowledge-powered blockchain be involved in the AI industry?" The rapid development in the AI space has kept both optimists and pessimists on their toes. With this in mind, it is essential to build AI systems and applications that are cryptographically guaranteed, which is the main idea behind the MinaZKML library.
MinaZKML is a library that allows you to:
- Convert AI models in the widely adopted ONNX (Open Neural Network Exchange) format into zero-knowledge proof circuits.
- Generate a Mina proof of an AI inference job executed on private inputs, ensuring the result is verifiable and privacy-preserving.
- Submit these proofs to the Mina blockchain, where they can be verified in a trustless, decentralized manner.
- Verify model execution without exposing any proprietary or sensitive data.
This means that you can trust the output of an AI model without having to reveal your sensitive inputs, whether you’re dealing with personal health data, financial advice, or any other sensitive computation.
- Convert AI models in the widely adopted ONNX (Open Neural Network Exchange) format into zero-knowledge proof circuits.
- Generate a Mina proof of an AI inference job executed on private inputs, ensuring the result is verifiable and privacy-preserving.
- Submit these proofs to the Mina blockchain, where they can be verified in a trustless, decentralized manner.
- Verify model execution without exposing any proprietary or sensitive data.
This means that you can trust the output of an AI model without having to reveal your sensitive inputs, whether you’re dealing with personal health data, financial advice, or any other sensitive computation.
The library is built with several key components designed to make the transition from AI model to
on-chain proof seamless, such as a prover in Rust, a command-line interface, examples in Python and Rust, a graph-based neural network framework, Onyx integration, and a cryptographic proof system.
for more in-depth information on how this works, please visit:
github.com/chris-chris/mi…
on-chain proof seamless, such as a prover in Rust, a command-line interface, examples in Python and Rust, a graph-based neural network framework, Onyx integration, and a cryptographic proof system.
for more in-depth information on how this works, please visit:
github.com/chris-chris/mi…
As it stands, AI future refinement is inevitable, and that is to say, the ability to run verifiable, privacy-preserving AI inference on-chain becomes increasingly essential. Mina’s zkML library aims to propel this progress by allowing anyone to generate a zero-knowledge proof from an AI inference job using private inputs, ensuring that the output is tamper-proof and your sensitive data remains secure.
What are your thoughts on this innovation, and are you interested in contributing?
Reference:
minaprotocol.com/blog/minas-zkm…
minaprotocol.com/blog/zero-know…
github.com/chris-chris/mi…
What are your thoughts on this innovation, and are you interested in contributing?
Reference:
minaprotocol.com/blog/minas-zkm…
minaprotocol.com/blog/zero-know…
github.com/chris-chris/mi…
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