Security & Privacy

FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions CVPR 2020 Fast and Private Submodular and k-Submodular Functions Maximization with Matroid Constraints ICML 2020 Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC NeurIPS 2020 GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators NeurIPS 2020 Interpretable and Differentially Private Predictions AAAI 2020 Introducing the VoicePrivacy Initiative Interspeech 2020 Inverting Gradients - How easy is it to break privacy in federated learning? NeurIPS 2020 Locally Differentially Private (Contextual) Bandits Learning NeurIPS 2020 Locally Private Hypothesis Selection COLT 2020 Obfuscation for Privacy-preserving Syntactic Parsing ACL 2020 On Log-Loss Scores and (No) Privacy EMNLP 2020 On the Equivalence between Online and Private Learnability beyond Binary Classification NeurIPS 2020 Orchard: Differentially Private Analytics at Scale OSDI 2020 Pairwise Learning with Differential Privacy Guarantees AAAI 2020 Pan-Private Uniformity Testing COLT 2020 Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder EMNLP 2020 Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning ICML 2020 Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption AAAI 2020 Privacy-Preserving Gradient Boosting Decision Trees AAAI 2020 PrivateEye: Scalable and Privacy-Preserving Compromise Detection in the Cloud NSDI 2020 Private Identity Testing for High-Dimensional Distributions NeurIPS 2020 Private-kNN: Practical Differential Privacy for Computer Vision CVPR 2020 Privately Learning Thresholds: Closing the Exponential Gap COLT 2020 Private Mean Estimation of Heavy-Tailed Distributions COLT 2020 Protecting Geolocation Privacy of Photo Collections AAAI 2020