Private machine learning done right (Ep. 207) episode artwork

EPISODE · Oct 25, 2022 · 26 MIN

Private machine learning done right (Ep. 207)

from Data Science at Home · host Francesco Gadaleta

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:https://www.intel.com/content/www/us/en/support/articles/000028173/processors.html  https://github.com/ayeks/SGX-hardware  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  

Episode metadata supplied by the publisher feed · Published Oct 25, 2022

Embed this episode

NOW PLAYING

Private machine learning done right (Ep. 207)

0:00 26:45

No transcript for this episode yet

We transcribe on demand. Request one and we'll notify you when it's ready — usually under 10 minutes.

No similar episodes found.

No similar podcasts found.

Frequently Asked Questions

How long is this episode of Data Science at Home?

This episode is 26 minutes long.

When was this Data Science at Home episode published?

This episode was published on October 25, 2022.

Can I download this Data Science at Home episode?

Yes. Use the download control on the episode player to save the publisher-provided media file.
URL copied to clipboard!