Who’s Afraid of Thomas Bayes?
Authors: Erick Galinkin | Published: 2021-07-30
Privacy Leakage
Blind Faith: Privacy-Preserving Machine Learning using Function Approximation
Authors: Tanveer Khan, Alexandros Bakas, Antonis Michalas | Published: 2021-07-29
Fully Homomorphically Encrypted Deep Learning as a Service
Authors: George Onoufriou, Paul Mayfield, Georgios Leontidis | Published: 2021-07-26
Reducing Risk of Model Inversion Using Privacy-Guided Training
Authors: Abigail Goldsteen, Gilad Ezov, Ariel Farkash | Published: 2020-06-29
Understanding Gradient Clipping in Private SGD: A Geometric Perspective
Authors: Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong | Published: 2020-06-27 | Updated: 2021-03-18
Prive-HD: Privacy-Preserved Hyperdimensional Computing
Authors: Behnam Khaleghi, Mohsen Imani, Tajana Rosing | Published: 2020-05-14
Privacy Leakage Avoidance with Switching Ensembles
Authors: Rauf Izmailov, Peter Lin, Chris Mesterharm, Samyadeep Basu | Published: 2019-11-18
Machine Learning with Membership Privacy using Adversarial Regularization
Authors: Milad Nasr, Reza Shokri, Amir Houmansadr | Published: 2018-07-16
Machine Learning Models that Remember Too Much
Authors: Congzheng Song, Thomas Ristenpart, Vitaly Shmatikov | Published: 2017-09-22
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Authors: Samuel Yeom, Irene Giacomelli, Matt Fredrikson, Somesh Jha | Published: 2017-09-05 | Updated: 2018-05-04