Ethereum Foundation Launches New Technology 'zkAPI' for Anonymous AI Usage Payments

Key Points of This Article

• Designed by Vitalik Buterin and others
• Both the provider and the payment layer remain undisclosed

Launch of Anonymous API Payments

On the 1st, the Ethereum Foundation announced that it has activated a technology called "zkAPI" on the Ethereum (ETH) mainnet, which allows for anonymous payments of API usage fees for AI (artificial intelligence) services using zero-knowledge proofs. According to the foundation's official blog, it was built in collaboration with the Open Anonymity Project, which specializes in privacy technology development.

Users can deposit ETH or USDC into a smart contract for payment in advance and demonstrate their balance without revealing the details of the transaction through "zero-knowledge proofs," allowing them to complete payments without disclosing their identity. The server side only verifies the authenticity of the proof and issues a short-lived API key, ensuring that neither the payment layer nor the API provider can link the billing details to the user's identity.

This system is based on a design proposal co-authored by researcher Davide Klapis and Ethereum co-founder Vitalik Buterin, which was published in the Ethereum Research Forum.

Previously, API payments were linked to payment accounts, leading to the accumulation of input records on the provider's side for AI services, which posed a challenge.

Remaining Challenges

The official blog explained that if usage continues from the same fixed IP address, the gateway may associate request patterns. For users seeking higher anonymity, it recommends using Tor (anonymizing communication network) to utilize a new connection for each session.

If the same personal information, writing style, or past conversation history is repeatedly shown, there is a risk that the AI service provider may link sessions together. While interactions conducted independently increase anonymity, it becomes difficult to obtain responses that consider past contexts.

The team mentioned that one of the mitigation strategies is to use a local or confidential computing environment model to draft instead of manual input.

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