What is EZKL?
EZKL is revolutionizing the landscape of zero-knowledge proofs by making them more accessible and user-friendly. At its core, EZKL simplifies the process of setting up zero-knowledge provers and verifiers by taking a high-level description of a program and automating the setup process. This empowers developers to leverage zero-knowledge proofs without the need for specialized knowledge or expertise in cryptography. The focus of EZKL lies in enabling zero-knowledge proofs for programs expressed as PyTorch AI/ML models and other computational graphs. These proofs are designed to be trusted by anyone with a copy of the verifier, and they can be directly verified on Ethereum and compatible chains. EZKL offers seamless integration with Python, allowing developers to utilize its capabilities directly from their Python environment through simple and intuitive bindings.
What role does AI play in EZKL?
AI, particularly in the form of PyTorch AI/ML models, plays a central role in EZKL's functionality. The project is specifically tailored to support programs expressed as AI/ML models and computational graphs, allowing developers to harness the power of AI while leveraging zero-knowledge proofs for privacy-preserving computations. By enabling zero-knowledge proofs for AI/ML models, EZKL empowers developers to conduct computations privately without compromising data confidentiality. This opens up a wide range of possibilities for deploying AI in sensitive or privacy-sensitive applications, such as healthcare, finance, and decentralized finance (DeFi), where data privacy is of paramount importance.
What are the main use cases enabled by EZKL?
EZKL enables several groundbreaking use cases, including:
- Privacy-Preserving AI: EZKL enables developers to deploy AI/ML models in a privacy-preserving manner by leveraging zero-knowledge proofs. This allows sensitive data to remain encrypted during computation, ensuring data privacy and confidentiality.
- Off-Chain Computation: EZKL facilitates moving large and complex computations off-chain in a way that is easy to program and manage. Developers can write their own functions in Python and use EZKL to set up zero-knowledge provers and verifiers for off-chain computation.
- No Predefined Function Limitation: Unlike some existing solutions, EZKL does not impose limitations on the types of functions that can be used. Developers have the flexibility to define and implement their own functions in Python, enabling a wide range of computation tasks to be performed privately.
- Scalability and Efficiency: EZKL boasts impressive performance metrics, proving an MNIST-sized inference in less than a second and under 180MB of memory. This scalability and efficiency make EZKL an attractive solution for privacy-preserving computation tasks, even on resource-constrained environments like the Ethereum Virtual Machine.
- Decentralization: EZKL promotes decentralization by eliminating the need for a centralized sequencer. Developers can utilize EZKL to perform computations off-chain without relying on a central authority, enhancing the resilience and trustlessness of their applications.
