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Differential Privacy

409 directly classified papers

Papers per year

Papers

Noisy Dual Mirror Descent: A Near Optimal Algorithm for Jointly-DP Convex Resource Allocation NIPS 2024
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model AISTATS 2024
Differentially Private Reward Estimation with Preference Feedback AISTATS 2024
The Relative Gaussian Mechanism and its Application to Private Gradient Descent AISTATS 2024
DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction) AAAI 2024
Differentially Private Set Representations NIPS 2024
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates AISTATS 2024
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning NIPS 2024
Differentially Private Video Activity Recognition WACV 2024
Differentially private methods for managing model uncertainty in linear regression JMLR 2024
Faster Rates of Differentially Private Stochastic Convex Optimization JMLR 2024
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification NIPS 2024
Truthful High Dimensional Sparse Linear Regression NIPS 2024
Locally Differentially Private In-Context Learning COLING 2024
Generate Synthetic Text Approximating the Private Distribution with Differential Privacy IJCAI 2024
The Limits of Differential Privacy in Online Learning NIPS 2024
A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility AAAI 2024
Noise-Aware Differentially Private Regression via Meta-Learning NIPS 2024
Revisiting Differentially Private ReLU Regression NIPS 2024
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training AISTATS 2024
Differentially Private Optimization with Sparse Gradients NIPS 2024
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions NIPS 2024
Privacy-Enhancing Person Re-Identification Framework - A Dual-Stage Approach WACV 2024
Towards the Robustness of Differentially Private Federated Learning AAAI 2024
On the Computational Complexity of Private High-dimensional Model Selection NIPS 2024