Do Androids Dream of Zero Knowledge?

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

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

This is where zero-knowledge proofs come into play. At its core, ZK proofs allow one party to prove to the other that a specific calculation was performed correctly without exposing the actual data or without requiring the verifier to redo the calculations (also called the brevity property) . Think of it like a Sudoku puzzle: while solving it can be tricky, verifying the solution is much easier.

This feature is especially valuable when computing tasks occur off-chain to avoid overwhelming a network and incurring high costs. With ZK proofs, these off-chain tasks can still be verified without burdening the blockchains, which have strict computation limits because all nodes must verify each block. In short, we need ZK cryptography to scale AI machine learning securely and efficiently.

ZK verifies ML models so we can safely scale AI

Machine learning, a subset of AI, is known for its heavy computational requirements, requiring massive amounts of data processing to simulate human adaptation and decision-making. From image recognition to predictive analytics, ML models are gearing up to transform almost every industry – if they haven’t already – but they’re also pushing the boundaries of computation. So how do we verify and confirm that ML models are authentic using blockchains, where onchain operations can be prohibitively expensive?

We need a demonstrable way to trust AI models so we know the model we’re using hasn’t been tampered with or falsely advertised. When you ask ChatGPT questions 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 responses dips here and there. However, in industries such as finance and healthcare, accuracy and reliability are crucial. One mistake could have had negative economic consequences around the world.

This is where ZK plays a crucial role. Using ZK proofs, ML calculations can still be performed off-chain, while also providing on-chain verification. This opens up new avenues for the use of AI models in blockchain applications. Zero-knowledge machine learning, or ZKML, enables cryptographic verification of ML algorithms and their output while the actual algorithms remain private, 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 in which an AI algorithm manages the rebalancing of assets to maximize returns while refining trading strategies. ZKML can perform 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 transactions. At the same time, ZK can protect users’ trading data, allowing them to maintain financial confidentiality even if the ML models they use to execute trades are public. The result? Secure AI-powered DeFi protocols with ZK verifiability.

We need to get to know our machines better

As AI becomes increasingly important to human activities, concerns about tampering, manipulation, and hostile attacks continue to increase. AI models, especially those that make critical decisions, must be resilient to attacks that would corrupt their output. Of course we want AI applications to be safe. It’s not just about AI safety in the traditional sense (i.e. ensuring models don’t behave unpredictably), but also about creating a trustworthy environment in which the model itself is easily verifiable.

In a world where models are becoming increasingly common, we live our lives essentially guided by AI. As the number of models grows, so does the likelihood of attacks that undermine the integrity of the model. This is especially concerning in scenarios where an AI model’s output may not be what it seems.

By integrating ZK cryptography into AI, we can now start building trust and accountability in these models. Like an SSL certificate or security badge in your web browser, there will likely be a symbol for AI verifiability: a symbol that guarantees that the model you’re interacting with is the one you expect.

In Blade Runnerthe Voight-Kampff test was intended to distinguish replicants from humans. Today, as we navigate an increasingly AI-driven world, we face a similar challenge: distinguishing authentic AI models from potentially compromised ones. In crypto, ZK cryptography could be our Voight-Kampff test: a robust, scalable method to verify the integrity of AI models without compromising their inner workings. That way, not only do we wonder if androids dream of electric sheep, but we also ensure that the AI ​​running our digital lives is exactly what it claims to be.

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