Vishleshak 🎩 🟪🟡
@vishleshak
(1/8) Let's talk about this new paper "ZKLoRA: Efficient Zero-Knowledge Proofs for LoRA Verification". This paper introduces ZKLoRA, a way to verify that small, fine-tuned updates (called LoRA weights) work with large AI models—without revealing the private details of those updates. Why does this matter? It allows people to collaborate on improving AI models securely, even if they don’t trust each other. It’s fast, taking just 1–2 seconds to verify updates, even for huge models.
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Vishleshak 🎩 🟪🟡
@vishleshak
(2/8) Imagine you’re working with a huge AI model, but you want to customize it for a specific task (like legal or medical advice). Instead of retraining the whole model, you can use LoRA—a lightweight way to fine-tune just a small part of the model. But here’s the problem: How do you know the LoRA update actually works without seeing the private details? And how does the person sharing the LoRA update make sure they get paid without giving away their secret work? ZKLoRA solves this by using zero-knowledge proofs—a cryptographic method that proves something is true without revealing the details.
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Vishleshak 🎩 🟪🟡
@vishleshak
(3/8) Let’s say you’re baking cookies with a secret recipe. You want to prove to your friend that your cookies are delicious, but you don’t want to share the recipe. ZKLoRA is like giving your friend a bite of the cookie and saying, “See? It’s good!”—without ever showing them the recipe. In the AI world, the “cookie” is the LoRA update, and the “recipe” is the private data behind it. ZKLoRA lets people share proof that their updates work without revealing their secrets.
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Vishleshak 🎩 🟪🟡
@vishleshak
(4/8) ZKLoRA has 3 steps: Multi-Party Inference: The AI model and the LoRA update work together to process data, but only partial information is shared. Proof Generation: The person with the LoRA update creates a cryptographic proof that their update works. Verification: The proof is checked in just 1–2 seconds to confirm the update is valid. This process ensures trust between both parties without revealing private details.
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