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

47 papers, page 1 of 1

1. ICML 2025Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning
2. ICML 2025Approximate Differential Privacy of the $\ell_2$ Mechanism
3. ICML 2025Auditing $f$-differential privacy in one run
4. ICML 2025Breaking the $n^{1.5}$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition
5. ICML 2025Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries
6. ICML 2025Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy
7. ICML 2025Certification for Differentially Private Prediction in Gradient-Based Training
8. ICML 2025Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
9. ICML 2025Differential Coding for Training-Free ANN-to-SNN Conversion
10. ICML 2025Differential Privacy Guarantees of Markov Chain Monte Carlo Algorithms
11. ICML 2025Differential Privacy Under Class Imbalance: Methods and Empirical Insights
12. ICML 2025Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference
13. ICML 2025Differentially Private Boxplots
14. ICML 2025Differentially Private Federated $k$-Means Clustering with Server-Side Data
15. ICML 2025Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model
16. ICML 2025DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
17. ICML 2025Empirical Privacy Variance
18. ICML 2025Faster Rates for Private Adversarial Bandits
19. ICML 2025Generalization of noisy SGD in unbounded non-convex settings
20. ICML 2025Going Deeper into Locally Differentially Private Graph Neural Networks
21. ICML 2025Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
22. ICML 2025Improving the Variance of Differentially Private Randomized Experiments through Clustering
23. ICML 2025It's My Data Too: Private ML for Datasets with Multi-User Training Examples
24. ICML 2025Leveraging Per-Instance Privacy for Machine Unlearning
25. ICML 2025Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
26. ICML 2025Lightweight Protocols for Distributed Private Quantile Estimation
27. ICML 2025Local Pan-privacy for Federated Analytics
28. ICML 2025Noisy SIGNSGD Is More Differentially Private Than You (Might) Think
29. ICML 2025On Differential Privacy for Adaptively Solving Search Problems via Sketching
30. ICML 2025On the Private Estimation of Smooth Transport Maps
31. ICML 2025Online Differentially Private Conformal Prediction for Uncertainty Quantification
32. ICML 2025Optimal Survey Design for Private Mean Estimation
33. ICML 2025Optimizing Noise Distributions for Differential Privacy
34. ICML 2025Plausible Token Amplification for Improving Accuracy of Differentially Private In-Context Learning Based on Implicit Bayesian Inference
35. ICML 2025Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
36. ICML 2025Privacy Amplification Through Synthetic Data: Insights from Linear Regression
37. ICML 2025Private Lossless Multiple Release
38. ICML 2025Private Model Personalization Revisited
39. ICML 2025Revisiting Differentially Private Algorithms for Decentralized Online Learning
40. ICML 2025Scalable Private Partition Selection via Adaptive Weighting
41. ICML 2025Scaling Laws for Differentially Private Language Models
42. ICML 2025Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs
43. ICML 2025The Role of Randomness in Stability
44. ICML 2025Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models
45. ICML 2025Towards Trustworthy Federated Learning with Untrusted Participants
46. ICML 2025Tracking The Best Expert Privately
47. ICML 2025When to Forget? Complexity Trade-offs in Machine Unlearning

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.