Скачать или смотреть Private AI & Homomorphic Encryption

As the world adopts Artificial Intelligence, the privacy risks are many. AI can improve our lives, but may leak our private data. Private AI is based on Homomorphic Encryption (HE), a new encryption paradigm which allows the cloud to operate on private data in encrypted form, without ever decrypting it, enabling private training and private prediction. Our 2016 ICML CryptoNets paper showed for the first time that it was possible to evaluate neural nets on homomorphically encrypted data, and opened new research directions combining machine learning and cryptography. The security of Homomorphic Encryption is based on hard problems in mathematics involving lattices, a candidate for post-quantum cryptography. This talk will explain Homomorphic Encryption, Private AI, and explain HE in action.

PRESENTED BY: Dr. Kristin Lauter is West Coast Head of Research Science for Facebook AI Research (FAIR), leading the Seattle and Menlo Park Labs with groups in Core Machine Learning, Computer Vision, Robotics, Natural Language Processing, and other areas. Her research focuses on Private AI, homomorphic encryption, and post-quantum cryptography. She spent 22 years at Microsoft Research, as a Principal Researcher and Partner Research Manager of Cryptography and Privacy Research.