blindai VS L2

We are working on an open-source project, BlindAI (https://github.com/mithril-security/blindai) to answer exactly that: privacy when sending data to remote AI models.

BlindAI (https://github.com/mithril-security/blindai) is an open-source confidential AI deployment. By using secure enclaves (Intel SGX for now, soon AMD SEV and Nvidia Confidential Computing), we provide end-to-end protection for users’ data, even when sending it to the Cloud for AI inference. You can see the gains of BlindAI on the scheme below:

Good thing with enclave is that the hardware protection enable us to use regular AES to secure communication with the enclave, which means no ciphertext expansion and lightweight client side. We do not need to have a complicated client side, we just need a slightly modified TLS client with additional security checks, like remote attestation but you can have a look on our client side it's light (https://github.com/mithril-security/blindai/tree/master/client).

I am glad to introduce BlindAI, an AI deployment solution, leveraging secure enclaves, to make remotely hosted AI models privacy friendly. We leverage the tract project as our inference engine to serve AI models in ONNX format inside an enclave. We also use the Rust SGX SDK to use Rust for our secure enclave for AI.

this is really cool!!! btw i also made a very small toy library years ago in rust too !https://github.com/bilal2vec/L2

this is really cool! i wrote a library a lot like this last year (https://github.com/bilal2vec/L2) and wrote up a very WIP blog post (https://bilal2vec.github.io/blog/rust/2020/08/02/writing-a-machine-learning-library-in-rust.html) about how i made it if y'all are interested ;)