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Differential Privacy

409 directly classified papers

Papers per year

Papers

Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives NIPS 2024
Differentially Private Equivalence Testing for Continuous Distributions and Applications NIPS 2024
Credit Attribution and Stable Compression NIPS 2024
Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime NIPS 2024
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model AISTATS 2024
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training AISTATS 2024
Pre-training Differentially Private Models with Limited Public Data NIPS 2024
Differential Privacy in Scalable General Kernel Learning via $K$-means Nystr{\"o}m Random Features NIPS 2024
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data AISTATS 2024
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift NIPS 2024
Private Geometric Median NIPS 2024
Private and Personalized Frequency Estimation in a Federated Setting NIPS 2024
Federated Experiment Design under Distributed Differential Privacy AISTATS 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
Online Distribution Learning with Local Privacy Constraints AISTATS 2024
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification NIPS 2024
Truthful High Dimensional Sparse Linear Regression NIPS 2024
Universal Exact Compression of Differentially Private Mechanisms NIPS 2024
Differentially Private Set Representations NIPS 2024
Differentially Private Reinforcement Learning with Self-Play NIPS 2024
Instance-Optimal Private Density Estimation in the Wasserstein Distance NIPS 2024
Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy NIPS 2024
Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust NIPS 2024
Faster Rates of Differentially Private Stochastic Convex Optimization JMLR 2024