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

146 papers, page 2 of 3

51. ICML 2025Faster Rates for Private Adversarial Bandits
52. ICML 2025Generalization of noisy SGD in unbounded non-convex settings
53. ICML 2025Going Deeper into Locally Differentially Private Graph Neural Networks
54. ICML 2025Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
55. ICML 2025Improving the Variance of Differentially Private Randomized Experiments through Clustering
56. ICML 2025It's My Data Too: Private ML for Datasets with Multi-User Training Examples
57. ICML 2025Leveraging Per-Instance Privacy for Machine Unlearning
58. ICML 2025Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
59. ICML 2025Lightweight Protocols for Distributed Private Quantile Estimation
60. ICML 2025Local Pan-privacy for Federated Analytics
61. ICML 2025Noisy SIGNSGD Is More Differentially Private Than You (Might) Think
62. ICML 2025On Differential Privacy for Adaptively Solving Search Problems via Sketching
63. ICML 2025On the Private Estimation of Smooth Transport Maps
64. ICML 2025Online Differentially Private Conformal Prediction for Uncertainty Quantification
65. ICML 2025Optimal Survey Design for Private Mean Estimation
66. ICML 2025Optimizing Noise Distributions for Differential Privacy
67. ICML 2025Plausible Token Amplification for Improving Accuracy of Differentially Private In-Context Learning Based on Implicit Bayesian Inference
68. ICML 2025Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
69. ICML 2025Privacy Amplification Through Synthetic Data: Insights from Linear Regression
70. ICML 2025Private Lossless Multiple Release
71. ICML 2025Private Model Personalization Revisited
72. ICML 2025Revisiting Differentially Private Algorithms for Decentralized Online Learning
73. ICML 2025Scalable Private Partition Selection via Adaptive Weighting
74. ICML 2025Scaling Laws for Differentially Private Language Models
75. ICML 2025Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs
76. ICML 2025The Role of Randomness in Stability
77. ICML 2025Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models
78. ICML 2025Towards Trustworthy Federated Learning with Untrusted Participants
79. ICML 2025Tracking The Best Expert Privately
80. ICML 2025When to Forget? Complexity Trade-offs in Machine Unlearning
81. NeurIPS 2025A Generalized Binary Tree Mechanism for Private Approximation of All-Pair Shortest Distances
82. NeurIPS 2025A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input
83. NeurIPS 2025Adaptive Data Analysis for Growing Data
84. NeurIPS 2025Adaptive Sigmoid Clipping for Balancing the Direction–Magnitude Mismatch Trade-off in Differentially Private Learning
85. NeurIPS 2025An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
86. NeurIPS 2025Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
87. NeurIPS 2025Boundary-Value PDEs Meet Higher-Order Differential Topology-aware GNNs
88. NeurIPS 2025Collapsing Taylor Mode Automatic Differentiation
89. NeurIPS 2025Continual Release Moment Estimation with Differential Privacy
90. NeurIPS 2025Continuous Simplicial Neural Networks
91. NeurIPS 2025Controlling The Spread of Epidemics on Networks with Differential Privacy
92. NeurIPS 2025Convex Approximation of Two-Layer ReLU Networks for Hidden State Differential Privacy
93. NeurIPS 2025Dense Metric Depth Estimation via Event-based Differential Focus Volume Prompting
94. NeurIPS 2025Differential Privacy for Euclidean Jordan Algebra with Applications to Private Symmetric Cone Programming
95. NeurIPS 2025Differential Privacy on Fully Dynamic Streams
96. NeurIPS 2025Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
97. NeurIPS 2025Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
98. NeurIPS 2025Differentially Private Gomory-Hu Trees
99. NeurIPS 2025Differentially Private High-dimensional Variable Selection via Integer Programming
100. NeurIPS 2025Differentially Private Quantiles with Smaller Error

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.