Differential Privacy papers, 2023–2026

Key findings

• Differential Privacy fell from 117 papers in 2023 to 103 in 2026, so interest is cooling.
• NeurIPS publishes the most Differential Privacy work: 217 papers from 2023 to 2026, 45% of the four-venue total.
• The fastest-growing overlap is with Social aspects · accountability transparency and interpretability: papers tagged with both rose from 0 in 2023 to 22 in 2026.
• Among the 5 topics under Federated and Privacy, it ranks #3 by 2026 paper count; the fastest grower in the group, Secure Aggregation, is up 1.8x versus 0.9x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Unified Framework for Adversary-Aware Differential Privacy Bounds
• ICML 2026Optimal conversion from Rényi Differential Privacy to $f$-Differential Privacy
• ICLR 2026Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
• CVPR 2026Fully Decentralized Certified Unlearning
• NeurIPS 2025Continual Release Moment Estimation with Differential Privacy
• ICML 2025Leveraging Per-Instance Privacy for Machine Unlearning
• ICLR 2025Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
• CVPR 2025Focal Split: Untethered Snapshot Depth from Differential Defocus
• NeurIPS 2024$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning
• ICML 2024Delving into Differentially Private Transformer
• ICLR 2024Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
• CVPR 2024Affine Equivariant Networks Based on Differential Invariants
• NeurIPS 2023(Amplified) Banded Matrix Factorization: A unified approach to private training
• ICML 2023Bayesian Estimation of Differential Privacy
• ICLR 2023A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy
• CVPR 2023Learning To Generate Image Embeddings With User-Level Differential Privacy
• NeurIPS 2026Asymptotically Optimal Best Arm Identification with Fixed-Budget under Differential Privacy
• ICML 2026Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov–Arnold Networks
• ICLR 2026Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy
• CVPR 2026LDP-Slicing Local Differential Privacy for Images via Randomized Bit-Plane Slicing

All Differential Privacy papers

217 papers, page 3 of 5

101. NeurIPS 2024Differential Privacy in Scalable General Kernel Learning via $K$-means Nystr{\"o}m Random Features
102. NeurIPS 2024Differentially Private Graph Diffusion with Applications in Personalized PageRanks
103. NeurIPS 2024Differentially Private Optimization with Sparse Gradients
104. NeurIPS 2024Differentially Private Set Representations
105. NeurIPS 2024Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
106. NeurIPS 2024Dimension-free Private Mean Estimation for Anisotropic Distributions
107. NeurIPS 2024DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
108. NeurIPS 2024Efficient and Private Marginal Reconstruction with Local Non-Negativity
109. NeurIPS 2024Exactly Minimax-Optimal Locally Differentially Private Sampling
110. NeurIPS 2024Faster Algorithms for User-Level Private Stochastic Convex Optimization
111. NeurIPS 2024Faster Differentially Private Top-$k$ Selection: A Joint Exponential Mechanism with Pruning
112. NeurIPS 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
113. NeurIPS 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
114. NeurIPS 2024From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach
115. NeurIPS 2024HEPrune: Fast Private Training of Deep Neural Networks With Encrypted Data Pruning
116. NeurIPS 2024Improving Neural Network Surface Processing with Principal Curvatures
117. NeurIPS 2024Instance-Optimal Private Density Estimation in the Wasserstein Distance
118. NeurIPS 2024Instance-Specific Asymmetric Sensitivity in Differential Privacy
119. NeurIPS 2024Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
120. NeurIPS 2024Locally Private and Robust Multi-Armed Bandits
121. NeurIPS 2024Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
122. NeurIPS 2024Noisy Dual Mirror Descent: A Near Optimal Algorithm for Jointly-DP Convex Resource Allocation
123. NeurIPS 2024On Differentially Private Subspace Estimation in a Distribution-Free Setting
124. NeurIPS 2024On Differentially Private U Statistics
125. NeurIPS 2024On the Computational Complexity of Private High-dimensional Model Selection
126. NeurIPS 2024Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
127. NeurIPS 2024Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime
128. NeurIPS 2024Oracle-Efficient Differentially Private Learning with Public Data
129. NeurIPS 2024PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
130. NeurIPS 2024Pre-training Differentially Private Models with Limited Public Data
131. NeurIPS 2024Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
132. NeurIPS 2024Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
133. NeurIPS 2024Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry
134. NeurIPS 2024Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust
135. NeurIPS 2024Private Geometric Median
136. NeurIPS 2024Private Online Learning via Lazy Algorithms
137. NeurIPS 2024Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
138. NeurIPS 2024PrivCirNet: Efficient Private Inference via Block Circulant Transformation
139. NeurIPS 2024Provably Safe Neural Network Controllers via Differential Dynamic Logic
140. NeurIPS 2024Public-data Assisted Private Stochastic Optimization: Power and Limitations
141. NeurIPS 2024Reparameterization invariance in approximate Bayesian inference
142. NeurIPS 2024Revisiting Differentially Private ReLU Regression
143. NeurIPS 2024Sample-Efficient Private Learning of Mixtures of Gaussians
144. NeurIPS 2024Scalable DP-SGD: Shuffling vs. Poisson Subsampling
145. NeurIPS 2024Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators
146. NeurIPS 2024The Limits of Differential Privacy in Online Learning
147. NeurIPS 2024Truthful High Dimensional Sparse Linear Regression
148. NeurIPS 2024Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification
149. NeurIPS 2024Universal Exact Compression of Differentially Private Mechanisms
150. NeurIPS 2024Wasserstein convergence of Cech persistence diagrams for samplings of submanifolds

FAQ

How many Differential Privacy papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

479 in total: 117 in 2023, 113 in 2024, 146 in 2025, 103 in 2026.

How many Differential Privacy papers are at NeurIPS 2026?

NeurIPS 2026 has 23 Differential Privacy papers, versus 66 the year before.

How many Differential Privacy papers are at ICLR 2026?

ICLR 2026 has 31 Differential Privacy papers, versus 29 the year before.

How many Differential Privacy papers are at ICML 2026?

ICML 2026 has 42 Differential Privacy papers, versus 47 the year before.

How many Differential Privacy papers are at CVPR 2026?

CVPR 2026 has 7 Differential Privacy papers, versus 4 the year before.

Which conference publishes the most Differential Privacy papers?

NeurIPS, with 217 papers from 2023 to 2026.