There are many solutions to private machine learning. I am pretty confident when I say that the one we are speaking in this episode is probably one of the most feasible and reliable.
I am with Daniel Huynh, CEO of Mithril Security, a graduate from Ecole Polytechnique with a specialisation in AI and data science. He worked at Microsoft on Privacy Enhancing Technologies under the office of the CTO of Microsoft France. He has written articles on Homomorphic Encryptions with the CKKS explained series (https://blog.openmined.org/ckks-explained-part-1-simple-encoding-and-decoding/). He is now focusing on Confidential Computing at Mithril Security and has written extensive articles on the topic: https://blog.mithrilsecurity.io/.
In this show we speak about confidential computing, SGX and private machine learning
References
- Mithril Security: https://www.mithrilsecurity.io/
- BindAI GitHub: https://github.com/mithril-security/blindai
- Use cases for BlindAI:
- Deploy Transformers models with confidentiality: https://blog.mithrilsecurity.io/transformers-with-confidentiality/
- Confidential medical image analysis with COVID-Net and BlindAI: https://blog.mithrilsecurity.io/confidential-covidnet-with-blindai/
- Build a privacy-by-design voice assistant with BlindAI: https://blog.mithrilsecurity.io/privacy-voice-ai-with-blindai/
- Confidential Computing Explained: https://blog.mithrilsecurity.io/confidential-computing-explained-part-1-introduction/
- Confidential Computing Consortium: https://confidentialcomputing.io/
- Confidential Computing White Papers: https://confidentialcomputing.io/white-papers-reports/
- List of Intel processors with Intel SGX:
- Azure Confidential Computing VMs with SGX:
- Azure Docs: https://docs.microsoft.com/en-us/azure/confidential-computing/confidential-computing-enclaves
- How to deploy BlindAI on Azure: https://docs.mithrilsecurity.io/getting-started/cloud-deployment/azure-dcsv3
- Confidential Computing 101: https://www.youtube.com/watch?v=77U12Ss38Zc
- Rust: https://www.rust-lang.org/
- ONNX: https://github.com/onnx/onnx
- Tract, a Rust inference engine for ONNX models: https://github.com/sonos/tract
