差分隐私论文趋势 2023–2026
DP 训练
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 479 篇被归入该主题的论文。
主要结论
- 差分隐私论文从 2023 年的 117 篇降到 2026 年的 103 篇，热度在回落。
- NeurIPS 是发差分隐私论文最多的会议，2023–2026 年共 217 篇，占四个会议总量的 45%。
- 交叉方向里涨得最快的是社会层面 · accountability transparency and interpretability：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 22 篇。
- 在「联邦与隐私」的 5 个主题里，它的 2026 年论文数排第 3；同组增长最快的安全聚合是 1.8 倍，差分隐私是 0.9 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 67 | 21 | 27 | 2 | 117 |
| 2024 | 61 | 25 | 24 | 3 | 113 |
| 2025 | 66 | 29 | 47 | 4 | 146 |
| 2026 | 23 | 31 | 42 | 7 | 103 |
代表论文
按年份和会议均匀挑选，方便快速看到每一届的样貌
- 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
全部差分隐私论文
共 479 篇，第 9 / 10 页
- ICML 2023Locally Regularized Neural Differential Equations: Some Black Boxes were meant to remain closed!
- ICML 2023Multi-Task Differential Privacy Under Distribution Skew
- ICML 2023Neural Stochastic Differential Games for Time-series Analysis
- ICML 2023Online Local Differential Private Quantile Inference via Self-normalization
- ICML 2023Predicting Ordinary Differential Equations with Transformers
- ICML 2023Private Statistical Estimation of Many Quantiles
- ICML 2023Random Grid Neural Processes for Parametric Partial Differential Equations
- ICML 2023Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance
- ICML 2023Statistical Indistinguishability of Learning Algorithms
- ICML 2023Streaming Submodular Maximization with Differential Privacy
- ICML 2023The Price of Differential Privacy under Continual Observation
- ICML 2023The Saddle-Point Method in Differential Privacy
- NeurIPS 2023(Amplified) Banded Matrix Factorization: A unified approach to private training
- NeurIPS 2023$k$-Means Clustering with Distance-Based Privacy
- NeurIPS 2023A Privacy-Friendly Approach to Data Valuation
- NeurIPS 2023A Randomized Approach to Tight Privacy Accounting
- NeurIPS 2023A Smooth Binary Mechanism for Efficient Private Continual Observation
- NeurIPS 2023A Unified Fast Gradient Clipping Framework for DP-SGD
- NeurIPS 2023Adaptive Data Analysis in a Balanced Adversarial Model
- NeurIPS 2023Adaptive Privacy Composition for Accuracy-first Mechanisms
- NeurIPS 2023Adversarially Robust Distributed Count Tracking via Partial Differential Privacy
- NeurIPS 2023An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions
- NeurIPS 2023Better Private Linear Regression Through Better Private Feature Selection
- NeurIPS 2023Black-Box Differential Privacy for Interactive ML
- NeurIPS 2023Bounding training data reconstruction in DP-SGD
- NeurIPS 2023Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
- NeurIPS 2023Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
- NeurIPS 2023Counting Distinct Elements Under Person-Level Differential Privacy
- NeurIPS 2023Creating a Public Repository for Joining Private Data
- NeurIPS 2023Decision Tree for Locally Private Estimation with Public Data
- NeurIPS 2023Differentially Private Approximate Near Neighbor Counting in High Dimensions
- NeurIPS 2023Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
- NeurIPS 2023Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling
- NeurIPS 2023DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization
- NeurIPS 2023DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning
- NeurIPS 2023Dynamic Personalized Federated Learning with Adaptive Differential Privacy
- NeurIPS 2023Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models
- NeurIPS 2023Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
- NeurIPS 2023Fast Optimal Locally Private Mean Estimation via Random Projections
- NeurIPS 2023Faster approximate subgraph counts with privacy
- NeurIPS 2023Faster Differentially Private Convex Optimization via Second-Order Methods
- NeurIPS 2023Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
- NeurIPS 2023Functional Renyi Differential Privacy for Generative Modeling
- NeurIPS 2023Gaussian Differential Privacy on Riemannian Manifolds
- NeurIPS 2023Generalization in the Face of Adaptivity: A Bayesian Perspective
- NeurIPS 2023Have it your way: Individualized Privacy Assignment for DP-SGD
- NeurIPS 2023Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
- NeurIPS 2023k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
- NeurIPS 2023Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency
- NeurIPS 2023Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查差分隐私在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "differential-privacy"}
常见问题
- 2023–2026 年四大会议一共有多少篇差分隐私论文？
- 共 479 篇，按年份分别是 2023 年 117 篇、2024 年 113 篇、2025 年 146 篇、2026 年 103 篇。
- NeurIPS 2026 有多少篇差分隐私论文？
- NeurIPS 2026 有 23 篇差分隐私论文，上一届是 66 篇。
- ICLR 2026 有多少篇差分隐私论文？
- ICLR 2026 有 31 篇差分隐私论文，上一届是 29 篇。
- ICML 2026 有多少篇差分隐私论文？
- ICML 2026 有 42 篇差分隐私论文，上一届是 47 篇。
- CVPR 2026 有多少篇差分隐私论文？
- CVPR 2026 有 7 篇差分隐私论文，上一届是 4 篇。
- 哪个会议的差分隐私论文最多？
- NeurIPS，2023–2026 年共 217 篇。