💡 Hey y’all – EP.2 has come😎
since i promised y'all that in this EP i will talk about the topic: Verifiable ML Property Cards? Lamination Time.
so, I’m here to unpack it the way my brain understood it!! no fluff, just the real deal.🔥 (cook level:100%)
let's see what i have learned from this topic🔥🔥👇🏻
😑🎀-> What’s this talk even about?
We’ve all seen those model cards and datasheets floating around AI projects, right? Basically, they’re like “nutrition labels” for machine learning models which tell you what data was used, what the model can do, and sometimes how it was trained.
BUT HERE’S THE CATCH: They’re all based on trust. You hope the person publishing that card isn’t lying.
And in a world where AI regulations are about to go wild (Europe, U.S., everyone catching up), “hoping” isn’t good enough anymore!!
🧯 Sooo... we need something stronger
This talk hits this idea: what if model cards and datasheets could actually be verified? Like with hard evidence that a model was trained on ethical data, or behaves the way it claims?
That’s where ML property attestation comes in.😎
It’s basically the model trainer (the prover) showing evidence to the model user or buyer (the verifier) that “yes, this model was trained the right way.”
But how do you prove that without blowing up compute costs or building a whole new crypto protocol every time?🤔
Existing ML Property Attestation Mechanisms
- ML-based attestations are often not robust and can be error-prone.
- Cryptographic attestation mechanisms (like Zero Knowledge Proofs and Multi-Party Computation) are inefficient.
🔍 Why not just use crypto or regular ML?
Well, they looked into that too but!!!
- ML-based attestation? Too brittle and easy to trick
- Cryptographic proofs like ZKPs? Too slow!!! (13+ minutes for large models and also need to retraining model each times (eg MOC for distributional-property), so, not versatile>> require designing specific ZKPs for new properties>>
not reusable!!!!💀🔪
Enter Hardware-Assisted TEEs🔥🔥
To overcome these limitations, Trusted Execution Environments (TEEs) provide isolated execution environments and protected storage for sensitive data. These TEEs can create a remote attestation to prove that a model behaves as expected without revealing sensitive data.
Remote attestation is a process where the verifier checks the current state and behavior of the prover. It must meet two criteria:
- Authenticity: The evidence represents the real state of the prover.
- Freshness: The evidence reflects the current state of the prover.
Can TEEs Enable ML Property Attestation?
Recent developments in hardware, like Intel’s AMX extensions and NVIDIA’s H100 GPUs, make it feasible to run ML training and inference within TEEs efficiently. This means that the Laminator framework can verify ML properties using hardware-assisted attestations in a way that is both practical and scalable.
🛡️ Enter: TEEs & Laminator
This is where things start to get really clever.🔥🔥
They introduce this framework called Laminator, and it uses something called a Trusted Execution Environment (TEE).
Think of it like a secure little bubble inside a computer that:
- Keeps data isolated
- Seals info safely
- Can show others proof that “this is really what happened” (called remote attestation)
Contributions of Laminator
- It runs PyTorch inside a TEE using Gramine(on Intel SGX)
- A tool inside the TEE, called the measurer, checks specific ML properties
- The TEE creates a property card fragment with verifiable proof These get bundled together into an assertion package that anyone can independently verify
So Laminator is kind of the middle path: strong guarantees with much better performance.🔥🔥
Experimental Setup and Evaluation: The Laminator framework was tested against baseline setups to evaluate its overhead. The results show that Laminator performs efficiently, even with the extra layer of hardware-assisted attestation
Can TEEs Enable ML Property Attestation?
Recent developments in hardware, like Intel’s AMX extensions and NVIDIA’s H100 GPUs, make it feasible to run ML training and inference within TEEs efficiently. This means that the Laminator framework can verify ML properties using hardware-assisted attestations in a way that is both practical and scalable.
Future Work and Extensions🎀
They are looking ahead, the team plans to:
- Run Laminator natively on Intel TDX (removing the need for Gramine)
- Use GPUs (like the H100) instead of the current CPU-only implementation
- Support more advanced models, including LLMs and text-to-image diffusion models
- Implement run-time property attestations for real-time verification
- Enable verification for multiple entities and organizations, creating a more distributed ecosystem for AI model validation
🧵 Wrap-Up: What did I learn?
- 🔒 Verifiable ML property cards aren’t just nice-to-have anymore!! they’re gonna be required.
- 🔧 Current tools are either too weak or too slow.
- 🛠️ Laminator hits that sweet spot using TEEs to create property cards you can actually trust.
- 🚀 It’s efficient, scalable, versatile, and pretty much made for the regulatory world we’re heading into.
🔧 Summary
In the end, Laminator provides a much-needed solution for verifiable ML property cards, preventing malicious model providers from falsely claiming properties about their models. By utilizing hardware-assisted attestations via TEEs, it makes the process of verifying model properties efficient, scalable, and versatile. With the growing need for regulations in AI, Laminator is well-positioned to address these concerns and play a key role in the future of AI accountability.
Wow, we’re already done with this episode! What an interesting dive into the world of verifiable ML property cards and Laminator. We explored how TEEs can help us trust the AI models we’re working with. This talk was jam-packed with insights. It’s clear that the world of AI verification is evolving fast, and Laminator might just be the key to solving some of the toughest challenges we’ll face.
This is just the beginning, though. New technologies like Intel’s AMX extensions and NVIDIA’s H100 GPUs are paving the way for a more secure and verifiable future. Exciting, right?😎
The future is wide open, and we’ve barely scratched the surface. There’s so much more to explore, from generative models to distributed property attestations. So, stick around for more deep dives. It’s a wild ride ahead! 🚀
See y’all in the next one 👋
Stay laminated. Stay verified.
— Episode 2 in the books 🔥