5 Use Cases for Zero-Knowledge Proofs in Machine Learning
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5 Use Cases for Zero-Knowledge Proofs in Machine Learning

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6 months ago

Op-Ed: Zero-knowledge proofs could provide safeguards as machine learning technology reaches its full potential.

5 Use Cases for Zero-Knowledge Proofs in Machine Learning

目录

Machine learning models rely on massive amounts of data to churn out accurate outputs that seem almost magical in their ability to understand us. But, as the amount of data ingested into machine learning projects grows, privacy flaws will become increasingly apparent — and there may be a tipping point where users are no longer willing to trade their privacy for the output.

Before the industry develops further, models need to have a layer of accountability built in. Users need to trust that their data won’t be abused, that the model hasn’t been altered or hacked, and that models meet rigorous data privacy standards. Right now, the standard isn’t being met — but we have the opportunity to change that.

Zero-knowledge proofs are one way for developers to create and run machine learning models that prove a computation was done correctly, while being free to choose which properties to make public. The result is the best of both worlds: personalized outputs based on secure, trustworthy models.

We see a growing number of real world use cases for zero-knowledge proofs that could provide safeguards as the technology reaches its full potential.

Real life applications for zero-knowledge proofs

Machine learning will touch every part of our lives in the future, whether we want it to or not. We’re currently in the unique position of being able to add a layer of security in several key use cases, both on-chain (public transactions carried out on a blockchain) and off-chain (transactions confirmed outside of the main blockchain network).

On-chain use cases

Creating privacy-preserving credit scores

When crypto traders borrow, the lender needs to be sure the borrower is trustworthy. Borrowers may be operating internationally and even pseudonymously. A machine learning model can be used to assess the creditworthiness of borrowers in a way that preserves privacy, so borrowers can be matched with lenders that best meet their needs.

Building private know-your-customer (KYC) processes

As part of the KYC process, new users are often asked to upload a photo of their driver’s license and then perform some form of a liveness test, involving looking into their webcam and turning their head. To protect the user’s personal privacy, a machine learning model could be built leveraging zero knowledge that performs the liveness test, checks it against a user’s driver's license photo, and returns a score for how closely they match along with a proof indicating the model was run accurately.

Generating accurate stablecoin exchange rates

Oracles serve an essential purpose by bringing off-chain data on-chain, which is important because smart contracts can’t get to information outside of the blockchain network. They are often used in stablecoins, where the security assumptions rely on regular and timely reporting of an exchange rate between an asset (for example, ETH) and a stablecoin which is supposed to remain pegged to a real-world currency. Machine learning can help make this reporting more accurate and robust, while zero-knowledge proofs guarantee that the oracle performed the computation correctly.

Off-chain use cases

Safeguarding the use of machine learning in high-assurance industries

When lives depend on it, it’s critical to know that machine learning models produce trustworthy results that haven’t been altered or hacked by malicious actors. Any machine learning models used in the military, AI-piloted cars, or medical imaging and diagnostics, for example, should have extra validation software that can validate a zero-knowledge proof that sensor input has been analyzed correctly.

Protecting proprietary machine learning models

Many companies have already built private machine-learning models that they don’t want to share publicly. Since these models may have been trained on proprietary data or used in highly regulated industries, it may become important to prove that a result came from that specific model.

Conclusion

As machine learning and AI enter our daily lives, it’s more important than ever for us to trust the models and data used to generate outputs that impact the safety of our world.

Platforms like Aleo make developing zero-knowledge algorithms simple thanks to the private by default, programmable platform. Machine learning developers can focus on doing what they do best: creating the algorithms and models that provide new insights into our world.

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Frank Chen is the Head of Product at Aleo —  the leading zero-knowledge proof-based developer platform for building fully-private, scalable, and cost-effective decentralized applications — where he leads consumer product efforts. Prior to Aleo, he was at Gitcoin, where he built the first instance of a quadratic funding mechanism with Vitalik Buterin and led 10+ rounds of quadratic funding for Ethereum public goods. Frank is an avid cook, writer, jiu-jitsu competitor and coach.

Aleo is the leading developer platform for building fully-private, scalable, and cost-effective decentralized applications. Using zero-knowledge cryptography, Aleo moves smart contract execution off-chain to enable new use cases like identity, finance, and gaming, scaling to thousands of transactions per second. Built on a decentralized and permissionless blockchain, Aleo brings the flexibility of Ethereum with a more scalable architecture that's designed from the ground up for privacy.

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