隐私攻击论文趋势 2023–2026

主要结论

• 隐私攻击论文从 2023 年的 124 篇增长到 2026 年的 219 篇，三年间扩大到约 1.8 倍。
• ICLR 是发隐私攻击论文最多的会议，2023–2026 年共 262 篇，占四个会议总量的 38%。
• 交叉方向里涨得最快的是对齐与安全：同时属于两个主题的论文从 2023 年的 2 篇增至 2026 年的 80 篇。
• 在「联邦与隐私」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的安全聚合是 1.8 倍，隐私攻击是 1.8 倍。

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 Private Empirical Defense Against Privacy Audits
• ICML 2026A Statistical Framework for Analyzing Specification Resistance to Learnware-Inversion Risks
• ICLR 2026(Token-Level) \textbf{InfoRMIA}: Stronger Membership Inference and Privacy Assessment for LLMs
• CVPR 2026Out of Sight Out of Track Adversarial Attacks on Propagation-based
• NeurIPS 2025A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation
• ICML 2025A Certified Unlearning Approach without Access to Source Data
• ICLR 2025Adversarial Attacks on Data Attribution
• CVPR 2025Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attacks
• NeurIPS 2024Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers
• ICML 2024Collective Certified Robustness against Graph Injection Attacks
• ICLR 2024Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
• CVPR 2024Efficient Privacy-Preserving Visual Localization Using 3D Ray Clouds
• NeurIPS 2023Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
• ICML 2023Data Poisoning Attacks Against Multimodal Encoders
• ICLR 2023Adversarial Attacks on Adversarial Bandits
• CVPR 2023Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders
• NeurIPS 2026Adaptive Target-Charging with Privacy Filters and Individual Accounting
• ICML 2026How does Bayesian Sampling help Membership Inference Attacks?
• ICLR 2026Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy
• CVPR 2026Revisiting Geometric Obfuscation with Dual Convergent Lines for Privacy-Preserving Image

全部隐私攻击论文

共 46 篇，第 1 / 1 页

1. NeurIPS 2023$k$-Means Clustering with Distance-Based Privacy
2. NeurIPS 2023A Unified Detection Framework for Inference-Stage Backdoor Defenses
3. NeurIPS 2023A Unified Fast Gradient Clipping Framework for DP-SGD
4. NeurIPS 2023Adaptive Privacy Composition for Accuracy-first Mechanisms
5. NeurIPS 2023Adversarially Robust Distributed Count Tracking via Partial Differential Privacy
6. NeurIPS 2023Batchnorm Allows Unsupervised Radial Attacks
7. NeurIPS 2023Blurred-Dilated Method for Adversarial Attacks
8. NeurIPS 2023Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
9. NeurIPS 2023Bounding training data reconstruction in DP-SGD
10. NeurIPS 2023Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
11. NeurIPS 2023Counting Distinct Elements Under Person-Level Differential Privacy
12. NeurIPS 2023Data Minimization at Inference Time
13. NeurIPS 2023DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
14. NeurIPS 2023Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling
15. NeurIPS 2023Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
16. NeurIPS 2023Faster approximate subgraph counts with privacy
17. NeurIPS 2023FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning
18. NeurIPS 2023Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
19. NeurIPS 2023GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference
20. NeurIPS 2023Gaussian Differential Privacy on Riemannian Manifolds
21. NeurIPS 2023Gaussian Membership Inference Privacy
22. NeurIPS 2023Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
23. NeurIPS 2023k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
24. NeurIPS 2023Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency
25. NeurIPS 2023Label-Only Model Inversion Attacks via Knowledge Transfer
26. NeurIPS 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
27. NeurIPS 2023Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack
28. NeurIPS 2023Minimax Risks and Optimal Procedures for Estimation under Functional Local Differential Privacy
29. NeurIPS 2023On Computing Pairwise Statistics with Local Differential Privacy
30. NeurIPS 2023On Private and Robust Bandits
31. NeurIPS 2023Optimal Unbiased Randomizers for Regression with Label Differential Privacy
32. NeurIPS 2023Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
33. NeurIPS 2023Practical Differentially Private Hyperparameter Tuning with Subsampling
34. NeurIPS 2023Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
35. NeurIPS 2023Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?
36. NeurIPS 2023Privacy Auditing with One (1) Training Run
37. NeurIPS 2023Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
38. NeurIPS 2023QuadAttac$K$: A Quadratic Programming Approach to Learning Ordered Top-$K$ Adversarial Attacks
39. NeurIPS 2023Scalable Membership Inference Attacks via Quantile Regression
40. NeurIPS 2023Static and Sequential Malicious Attacks in the Context of Selective Forgetting
41. NeurIPS 2023Students Parrot Their Teachers: Membership Inference on Model Distillation
42. NeurIPS 2023The Target-Charging Technique for Privacy Analysis across Interactive Computations
43. NeurIPS 2023Understanding Deep Gradient Leakage via Inversion Influence Functions
44. NeurIPS 2023Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$-Differential Privacy
45. NeurIPS 2023User-Level Differential Privacy With Few Examples Per User
46. NeurIPS 2023What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?

常见问题

2023–2026 年四大会议一共有多少篇隐私攻击论文？

共 698 篇，按年份分别是 2023 年 124 篇、2024 年 180 篇、2025 年 175 篇、2026 年 219 篇。

NeurIPS 2026 有多少篇隐私攻击论文？

NeurIPS 2026 有 55 篇隐私攻击论文，上一届是 45 篇。

ICLR 2026 有多少篇隐私攻击论文？

ICLR 2026 有 102 篇隐私攻击论文，上一届是 83 篇。

ICML 2026 有多少篇隐私攻击论文？

ICML 2026 有 38 篇隐私攻击论文，上一届是 29 篇。

CVPR 2026 有多少篇隐私攻击论文？

CVPR 2026 有 24 篇隐私攻击论文，上一届是 18 篇。

哪个会议的隐私攻击论文最多？

ICLR，2023–2026 年共 262 篇。