Vitaly Shmatikov, How to Salvage Federated Learning
Virtual Seminar, Interest Group on Privacy and Machine Learning
Видео Vitaly Shmatikov, How to Salvage Federated Learning канала Privacy and Security in ML Interest Group
Видео Vitaly Shmatikov, How to Salvage Federated Learning канала Privacy and Security in ML Interest Group
Показать
Комментарии отсутствуют
Информация о видео
22 января 2021 г. 20:07:40
00:42:46
Другие видео канала
Fatemeh Mireshghallah (UCSD)
Alina Oprea, Machine Learning Integrity and Privacy in Adversarial Environments
James Bell (Turing), Secure Single-Server Aggregation with (Poly)Logarithmic Overhead
Yizheng Chen (U of Maryland), Continuous Learning for Android Malware Detection
Ahmed Salem (Microsoft Research), Adversarial Exploration of Machine Learning Models' Accountability
Privacy Linter and Opacus: Privacy Attacks and Differentially Private Training Open-Source Libraries
Jacob Steinhardt (UC Berkeley), The Science of Measurement in Machine Learning
Graham Cormode, Towards Federated Analytics with Local Differential Privacy
Jamie Hayes (DeepMind), Towards Transformation-Resilient Provenance Detection
Nicolas Papernot, What does it mean for ML to be trustworthy?
Eugene Bagdasaryan (Cornell Tech), Blind Backdoors in Deep Learning
Natasha Fernandes (UNSW), Quantitative Information Flow Refinement Orders and Application to DP
Dr. Sara Hooker (Google Brain), The myth of interpretable, robust, compact and high performance DNNs
Matthew Jagielski (Google Research), Some Results on Privacy and Machine Unlearning
Shawn Shan, Security beyond Defenses: Protecting DNN systems via Forensics and Recovery
Ben Y. Zhao (University of Chicago), Adversarial Robustness via Forensics in Deep Neural Networks
Florian Tramer (Google Brain)
Giulia Fanti (CMU), Tackling Data Silos with Synthetic Data
Konrad Rieck, Adversarial Preprocessing: Image-Scaling Attacks in Machine Learning.
Tianhao Wang (University of Virginia), Continuous Release of Data Streams under Differential Privacy