Do androids dream of zero-knowledge?

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In the famous opening scene of Blade Runner, a character named Holden administers a fictional interpretation of the Turing test to gauge whether Leon is a replicant (humanoid robot). For the test, Holden tells Leon a story to evoke an emotional response. “You’re in a desert, you’re walking on the sand, suddenly you look down… you look down and you see a turtle, Leon. He’s crawling towards you…” As Holden continues to tell this hypothetical story, Leon becomes increasingly agitated until it becomes clear that he is not human.

We’re not in Blade Runner territory yet in the real world, but as AI and machine learning become more integrated into our lives, we need assurance that the AI ​​models we use are what they say they are.

This is where zero knowledge proofs come into play. In essence, ToM proofs allow one party to prove to another that a particular calculation was executed correctly without revealing the actual data or requiring the verifier to redo the calculations (aka concise specification). Think of it like a sudoku puzzle: While it may be difficult to solve, it’s much easier to verify the solution.

This feature is particularly valuable when computational tasks occur off-chain to avoid overloading a network and incurring high fees. With ZK proofs, these off-chain tasks can be verified without burdening blockchains, which have strict computational limits since all nodes must verify each block. In short, we need ZK encryption to scale AI machine learning safely and efficiently.

ZK validates machine learning models so we can safely scale AI

Machine learning, a subset of artificial intelligence, is known for its heavy computational requirements, requiring processing vast amounts of data to simulate human adaptation and decision-making. ML models are poised to transform almost every industry, from image recognition to predictive analytics (if they haven’t already), but they’re also pushing the boundaries of computing. So how can we verify and certify the authenticity of ML models using blockchains, where on-chain transactions can be prohibitively expensive?

We need a provable way to trust AI models to ensure that the model we use has not been falsified or misadvertised. When you make ChatGPT queries about your favorite sci-fi movies, you probably trust the model used, and it’s not the end of the world if the quality of the answers drops here and there. However, in industries such as finance and healthcare, accuracy and reliability are critical. A single mistake could have caused a cascade of negative economic impacts around the world.

This is where ToM plays an important role. Leveraging ToM proofs, machine learning calculations can be executed off-chain while having on-chain validation. This opens new avenues for deploying AI models in blockchain applications. Zero-knowledge machine learning, or ZKML, allows cryptographic verification of ML algorithms and their outputs while keeping the actual algorithms secret, bridging the gap between the computational demands of AI and the security guarantees of blockchain.

One of the most exciting ZKML applications is DeFi. Imagine a liquidity pool where an AI algorithm manages the rebalancing of assets to maximize return while also improving trading strategies. ZKML can run these calculations off-chain and then use ZK proofs to ensure that an ML model is legitimate, rather than another algorithm or someone else’s actions. At the same time, ZK protects users’ trading data so they can maintain financial privacy even if the ML models they use to trade are publicly available. Conclusion? Secure AI-powered DeFi protocols with ZK verifiability.

We must know our machines better

As AI becomes more central to human activities, concerns about tampering, manipulation, and hostile attacks continue to grow. AI models, especially models that deal with critical decisions, must be resilient to attacks that corrupt their output. Of course, we want artificial intelligence applications to be safe. This isn’t just about AI security in the traditional sense (i.e. ensuring models don’t behave unpredictably), it’s also about creating an untrustworthy environment in which the model itself can be easily verified.

In a world where models proliferate, we actually live our lives under the guidance of artificial intelligence. As the number of models increases, the potential for attacks that weaken the integrity of the model also increases. This is especially concerning in scenarios where the output of an AI model may not be what it seems.

By integrating ZK encryption into AI we can now begin to build trust and accountability in these models. It will likely be a symbol of the AI’s verifiability, like an SSL certificate or security badge on your web browser; There will be a symbol that guarantees that the model you are interacting with is the model you expected.

In Blade Runner, the Voight-Kampff test was intended to distinguish replicants from humans. Today, as we navigate a world increasingly driven by AI, we face a similar challenge: distinguishing genuine AI models from potentially compromised models. In crypto, ZK encryption can be used as our Voight-Kampff test, a powerful, scalable method to verify the integrity of AI models without compromising their inner workings. In this way, we not only ask whether androids dream of electric sheep, but also ensure that the artificial intelligence that guides our digital lives is exactly what it claims to be.

Rob Viglione

Rob Viglione is the co-founder and CEO of Horizen Labs, the development studio behind many leading web3 projects including zkVerify, Horizen, and ApeChain. Rob is deeply interested in web3 scalability, blockchain efficiency, and zero-knowledge proofs. His work focuses on developing innovative solutions for zk-rollups to increase scalability, save costs, and increase efficiency. He has a Ph.D. MBA in Finance, MBA in Finance and Marketing, and BSc in Physics and Applied Mathematics. Rob currently serves on the Board of Directors of the Puerto Rico Blockchain Trade Association.