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

117 papers, page 2 of 3

51. NeurIPS 2023(Amplified) Banded Matrix Factorization: A unified approach to private training
52. NeurIPS 2023$k$-Means Clustering with Distance-Based Privacy
53. NeurIPS 2023A Privacy-Friendly Approach to Data Valuation
54. NeurIPS 2023A Randomized Approach to Tight Privacy Accounting
55. NeurIPS 2023A Smooth Binary Mechanism for Efficient Private Continual Observation
56. NeurIPS 2023A Unified Fast Gradient Clipping Framework for DP-SGD
57. NeurIPS 2023Adaptive Data Analysis in a Balanced Adversarial Model
58. NeurIPS 2023Adaptive Privacy Composition for Accuracy-first Mechanisms
59. NeurIPS 2023Adversarially Robust Distributed Count Tracking via Partial Differential Privacy
60. NeurIPS 2023An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions
61. NeurIPS 2023Better Private Linear Regression Through Better Private Feature Selection
62. NeurIPS 2023Black-Box Differential Privacy for Interactive ML
63. NeurIPS 2023Bounding training data reconstruction in DP-SGD
64. NeurIPS 2023Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
65. NeurIPS 2023Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
66. NeurIPS 2023Counting Distinct Elements Under Person-Level Differential Privacy
67. NeurIPS 2023Creating a Public Repository for Joining Private Data
68. NeurIPS 2023Decision Tree for Locally Private Estimation with Public Data
69. NeurIPS 2023Differentially Private Approximate Near Neighbor Counting in High Dimensions
70. NeurIPS 2023Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
71. NeurIPS 2023Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling
72. NeurIPS 2023DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization
73. NeurIPS 2023DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning
74. NeurIPS 2023Dynamic Personalized Federated Learning with Adaptive Differential Privacy
75. NeurIPS 2023Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models
76. NeurIPS 2023Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
77. NeurIPS 2023Fast Optimal Locally Private Mean Estimation via Random Projections
78. NeurIPS 2023Faster approximate subgraph counts with privacy
79. NeurIPS 2023Faster Differentially Private Convex Optimization via Second-Order Methods
80. NeurIPS 2023Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
81. NeurIPS 2023Functional Renyi Differential Privacy for Generative Modeling
82. NeurIPS 2023Gaussian Differential Privacy on Riemannian Manifolds
83. NeurIPS 2023Generalization in the Face of Adaptivity: A Bayesian Perspective
84. NeurIPS 2023Have it your way: Individualized Privacy Assignment for DP-SGD
85. NeurIPS 2023Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
86. NeurIPS 2023k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
87. NeurIPS 2023Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency
88. NeurIPS 2023Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack
89. NeurIPS 2023Minimax Risks and Optimal Procedures for Estimation under Functional Local Differential Privacy
90. NeurIPS 2023Nearly Tight Bounds For Differentially Private Multiway Cut
91. NeurIPS 2023Neural Fields with Hard Constraints of Arbitrary Differential Order
92. NeurIPS 2023Neural Graph Generation from Graph Statistics
93. NeurIPS 2023Offline Reinforcement Learning with Differential Privacy
94. NeurIPS 2023On Computing Pairwise Statistics with Local Differential Privacy
95. NeurIPS 2023On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence
96. NeurIPS 2023On the Gini-impurity Preservation For Privacy Random Forests
97. NeurIPS 2023Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning
98. NeurIPS 2023Optimal Unbiased Randomizers for Regression with Label Differential Privacy
99. NeurIPS 2023Post-processing Private Synthetic Data for Improving Utility on Selected Measures
100. NeurIPS 2023Practical Differentially Private Hyperparameter Tuning with Subsampling

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.