Why zk-ML?

This is both exciting and a tad overwhelming. For those in the crypto realm, after admiring machine learning's capabilities, the instinct is to think about centralization risks and explore avenues to decentralize them into an openly verifiable and owned network. While current models ingest vast amounts of public text and data, few actually possess and control these models. The real question isn't just about AI's immense value, but "how can we design these systems so that every user not only gains economically but can also control how their data is used, respecting their privacy rights?" It's crucial to highlight that currently, in the realm of on-chain machine learning, the main application of zero-knowledge proofs is to confirm accurate computation. Specifically, SNARKs (Succinct Non-Interactive Arguments of Knowledge) are prized for their succinct attributes in the ML scenario. This value arises because zero-knowledge proofs safeguard Neural Network operators Intellectual Property while providing platform users accuracy that the prediction was performed correctly with a specific model - Prove computation happened correction, while anyone can verify. Imagine systems being able to digitally sign without a central authority. We're in the nascent stages of determining what is feasible to verify with zero-knowledge proofs on-chain. Yet, algorithmic advancements are broadening the scope of the following possibilities: Verify model authenticity - Zero knowledge proofs can prove a specific committed model is used, not an inferior substitute. This prevents bait-and-switch of models behind APIs. For example, a provider may claim to run an expensive high-accuracy model but actually serves a cheaper low-accuracy one to save costs. Without proofs, users cannot audit if the model matches what was committed. ZK proofs enable model owners to commit to a particular model, and users to verify model inputs match the commitment. This accountability ensures users get the authentic model they pay for, not a degraded cheaper version. Model Integrity: Ensuring that a specific machine learning algorithm operates uniformly on different users' data is essential. This uniformity is particularly crucial in areas like a blockchain credit assessments or variable loan collateralization while avoiding any potential biases. One way to achieve this is through functional commitments. By committing to a specific model and its parameters, individuals can submit their data, and the resulting output will confirm that the model executed with the predefined parameters for each dataset. Alternatively, by publicizing the model and its parameters, users can demonstrate they've used the appropriate model and parameters on their authenticated data. Attestations: Suppose you aim to incorporate endorsements from authenticated external entities, such as digital platforms or hardware capable of generating digital signatures, into a model or any on-chain smart contract. In this scenario, you'd authenticate the signature utilizing a zero-knowledge proof and subsequently utilize this proof as an input for the program.Proof of Account Abstraction & Proof of personhood: Ensuring the uniqueness of an individual without jeopardizing their privacy is paramount. This concept can be integrated with account abstraction, which seeks to simplify and standardize how accounts function in systems like blockchain, making them more flexible and general. By doing so, you can streamline user experience while preserving privacy. Firstly, establish a verification mechanism—this could be biometric scanning, an encrypted method for submitting government IDs, or even a unique abstracted account identifier. With the help of zero-knowledge proofs, validation can be conducted to confirm an individual's authentication without revealing specifics about their identity, be it a recognizable name or a pseudonymous identifier like a public key.

Incorporating account abstraction can enhance the flexibility of this approach. Instead of being tied to fixed identifiers, users can interact with systems using customizable logic without compromising security or privacy. This allows for a more seamless and private interaction within decentralized systems, combining the benefits of proof of personhood with the versatility of account abstraction. ZK KYC: Through Zero-Knowledge proofs, one can validate that an individual's identity matches the provided ID without revealing the actual ID data. Additionally, it ensures the ID is not on any sanction lists. While this method is currently not recognized by regulators as a primary KYC procedure. Prediction Markets: By leveraging compact machine learning models, it's feasible to classify textual content into several distinct categories. Imagine designing a smart contract that triggers a payment once a news article is identified as corresponding to a predicted event—be it an election result, the severity of a hurricane, or the emergence of a new COVID variant. For transparency and validation, any participant can download the verified article, execute the model on it, and then submit a proof of the outcome. Real Autonomous DAOs: Leveraging machine learning within DAOs paves the way for enhanced decision-making, fine-tuned governance mechanisms, and proposals that resonate with the community's collective goals, ensuring seamless operations and superior results. Incorporating Verified Inference is crucial to maintain the integrity of the governance process.