What You See is What You Get: Principled Deep Learning via Distributional Generalization
Authors: Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jarosław Błasiok, Preetum Nakkiran | Published: 2022-04-07 | Updated: 2022-10-17
プライバシー評価
User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition in Federated Learning
Authors: Tiantian Feng, Raghuveer Peri, Shrikanth Narayanan | Published: 2022-04-05 | Updated: 2022-05-17
Differentially Private Sampling from Rashomon Sets, and the Universality of Langevin Diffusion for Convex Optimization
Authors: Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay | Published: 2022-04-04 | Updated: 2023-08-28
Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning
Authors: Héber H. Arcolezi | Published: 2022-04-02
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
Authors: Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini | Published: 2022-03-31 | Updated: 2022-10-06
Differentially Private Federated Learning via Reconfigurable Intelligent Surface
Authors: Yuhan Yang, Yong Zhou, Youlong Wu, Yuanming Shi | Published: 2022-03-31
Privacy-Preserving Reinforcement Learning Beyond Expectation
Authors: Arezoo Rajabi, Bhaskar Ramasubramanian, Abdullah Al Maruf, Radha Poovendran | Published: 2022-03-18
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification
Authors: Dominique Mercier, Adriano Lucieri, Mohsin Munir, Andreas Dengel, Sheraz Ahmed | Published: 2021-11-29
Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques
Authors: Ashly Lau, Jonathan Passerat-Palmbach | Published: 2021-09-06
Private Multi-Task Learning: Formulation and Applications to Federated Learning
Authors: Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith | Published: 2021-08-30 | Updated: 2023-10-17