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 2 of 5

51. NeurIPS 2025Instance-Optimality for Private KL Distribution Estimation
52. NeurIPS 2025Learning Differential Pyramid Representation for Tone Mapping
53. NeurIPS 2025Locally Optimal Private Sampling: Beyond the Global Minimax
54. NeurIPS 2025Mitigating the Privacy–Utility Trade-off in Decentralized Federated Learning via f-Differential Privacy
55. NeurIPS 2025Multi-Class Support Vector Machine with Differential Privacy
56. NeurIPS 2025Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
57. NeurIPS 2025Neural Fractional Attention Differential Equations
58. NeurIPS 2025On Traceability in $\ell_p$ Stochastic Convex Optimization
59. NeurIPS 2025Online Locally Differentially Private Conformal Prediction via Binary Inquiries
60. NeurIPS 2025Online robust locally differentially private learning for nonparametric regression
61. NeurIPS 2025Optimal Best Arm Identification under Differential Privacy
62. NeurIPS 2025Optimal Regret of Bandits under Differential Privacy
63. NeurIPS 2025PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors
64. NeurIPS 2025Privacy amplification by random allocation
65. NeurIPS 2025Private Continual Counting of Unbounded Streams
66. NeurIPS 2025Private Evolution Converges
67. NeurIPS 2025Private Geometric Median in Nearly-Linear Time
68. NeurIPS 2025Private Hyperparameter Tuning with Ex-Post Guarantee
69. NeurIPS 2025Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings
70. NeurIPS 2025Private Set Union with Multiple Contributions
71. NeurIPS 2025Private Statistical Estimation via Truncation
72. NeurIPS 2025Private Training Large-scale Models with Efficient DP-SGD
73. NeurIPS 2025Private Zeroth-Order Optimization with Public Data
74. NeurIPS 2025Projective Equivariant Networks via Second-order Fundamental Differential Invariants
75. NeurIPS 2025Purifying Approximate Differential Privacy with Randomized Post-processing
76. NeurIPS 2025Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph
77. NeurIPS 2025Sequentially Auditing Differential Privacy
78. NeurIPS 2025Setting $\varepsilon$ is not the Issue in Differential Privacy
79. NeurIPS 2025Sketched Gaussian Mechanism for Private Federated Learning
80. NeurIPS 2025Solving Partial Differential Equations via Radon Neural Operator
81. NeurIPS 2025Sum Estimation under Personalized Local Differential Privacy
82. NeurIPS 2025Synthesize Privacy-Preserving High-Resolution Images via Private Textual Intermediaries
83. NeurIPS 2025The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
84. NeurIPS 2025Tight Bounds for Answering Adaptively Chosen Concentrated Queries
85. NeurIPS 2025Time-uniform and Asymptotic Confidence Sequence of Quantile under Local Differential Privacy
86. NeurIPS 2025UEPI: Universal Energy-Behavior-Preserving Integrators for Energy Conservative/Dissipative Differential Equations
87. NeurIPS 2025Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy
88. NeurIPS 2025Wavy Transformer
89. NeurIPS 2025⁠When Data Can't Meet: Estimating Correlation Across Privacy Barriers
90. NeurIPS 2024$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning
91. NeurIPS 2024A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
92. NeurIPS 2024Advection Augmented Convolutional Neural Networks
93. NeurIPS 2024Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters
94. NeurIPS 2024An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations
95. NeurIPS 2024Attack-Aware Noise Calibration for Differential Privacy
96. NeurIPS 2024Auditing Privacy Mechanisms via Label Inference Attacks
97. NeurIPS 2024Banded Square Root Matrix Factorization for Differentially Private Model Training
98. NeurIPS 2024Certified Machine Unlearning via Noisy Stochastic Gradient Descent
99. NeurIPS 2024Continual Counting with Gradual Privacy Expiration
100. NeurIPS 2024Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature

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.