Privacy Preserving Machine Learning with Fully Homomorphic Encryption, Jordan Frery @GoogleTechTalks

Discover Privacy Preserving Machine Learning (PPML) with Fully Homomorphic Encryption (FHE) with Jordan Frery, Zama Research Scientist, who gave a @GoogleTechTalks - 17/01/2023. -- Slides here: https://www.zama.ai/post/zama-concrete-ml-at-google-tech-talks -- Abstract: In today's digital age, protecting privacy has become increasingly difficult. However, new developments such as Fully Homomorphic Encryption (FHE) provide a means of safeguarding sensitive client information. We are excited to present Concrete-ML (https://github.com/zama-ai/concrete-ml), our open-source library that allows for the seamless conversion of Machine Learning (ML) models into their FHE counterparts. With our technology, clients can enjoy zero-trust interactions with service providers while also enabling the deployment of ML models on untrusted servers without compromising the privacy of user data. Presenter: Jordan Frery, Zama Research Scientist. Host: Google TechTalks (https://www.youtube.com/watch?v=-lhn2GdHhGc&list=PLSIUOFhnxEiDoTNvhZWIm1PNBAFJWUxU8&index=2) ------------------- Subscribe to our channel @zama_fhe for more FHE content! ➡️ Discover what we do: https://www.zama.ai/ ➡️ We are open-source: https://github.com/zama-ai Find us here also: LinkedIn: https://www.linkedin.com/company/zama-ai Twitter: https://twitter.com/zama_fhe Community forum: https://community.zama.ai/