← Application Areas
Machine Learning › Application Areas ›

Privacy

2794 directly classified papers

Papers per year

Papers

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs CVPR 2020
Learning from Mixtures of Private and Public Populations NIPS 2020
Hypothesis Testing Interpretations and Renyi Differential Privacy AISTATS 2020
Private Mean Estimation of Heavy-Tailed Distributions COLT 2020
Locally Private Hypothesis Selection COLT 2020
PAC learning with stable and private predictions COLT 2020
Truthful Data Acquisition via Peer Prediction NIPS 2020
Optimal Private Median Estimation under Minimal Distributional Assumptions NIPS 2020
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms NIPS 2020
Defending Against Model Stealing Attacks With Adaptive Misinformation CVPR 2020
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks CVPR 2020
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation EMNLP 2020
Efficient, Noise-Tolerant, and Private Learning via Boosting COLT 2020
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy JMLR 2020
On Log-Loss Scores and (No) Privacy EMNLP 2020
A Framework to Assist Chat Operators of Mental Healthcare Services EMNLP 2020
Practical Locally Private Heavy Hitters JMLR 2020
Private Query Release Assisted by Public Data ICML 2020
(Locally) Differentially Private Combinatorial Semi-Bandits ICML 2020
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models ICML 2020
Oracle Efficient Private Non-Convex Optimization ICML 2020
TextHide: Tackling Data Privacy in Language Understanding Tasks EMNLP 2020
Differentially Private Language Models Benefit from Public Pre-training EMNLP 2020
Evade Deep Image Retrieval by Stashing Private Images in the Hash Space CVPR 2020
FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions CVPR 2020