社会层面 · privacy论文趋势 2023–2026

主要结论

• 社会层面 · privacy论文从 2023 年的 21 篇增长到 2026 年的 138 篇，三年间扩大到约 6.6 倍。
• NeurIPS 是发社会层面 · privacy论文最多的会议，2023–2026 年共 215 篇，占四个会议总量的 50%。
• 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 93 篇。
• 在「对齐与负责任 AI」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的社会层面 · safety是 93 倍，社会层面 · privacy是 6.6 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 2026Assessing Per-Sample Membership Inference Vulnerability without Retraining
• ICML 2026Accuracy-First Rényi Differential Privacy and Post-Processing Immunity
• ICLR 2026Mitigating Privacy Risk via Forget Set-Free Unlearning
• CVPR 2026Protego User-Centric Pose-Invariant Privacy Protection Against Face Recognition-Induced Digital Footprint
• NeurIPS 2025A Generalized Binary Tree Mechanism for Private Approximation of All-Pair Shortest Distances
• ICML 2025Breaking the $n^{1.5}$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition
• ICLR 2025Machine Unlearning via Simulated Oracle Matching
• CVPR 2025Enhancing Facial Privacy Protection via Weakening Diffusion Purification
• NeurIPS 2024Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
• ICML 2024Privacy Preserving Adaptive Experiment Design
• ICLR 2024Beyond Memorization: Violating Privacy via Inference with Large Language Models
• CVPR 2024Validating Privacy-Preserving Face Recognition under a Minimum Assumption
• NeurIPS 2023Faster approximate subgraph counts with privacy
• ICML 2023GuardHFL: Privacy Guardian for Heterogeneous Federated Learning
• ICLR 2023Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
• CVPR 2023Privacy-Preserving Representations Are Not Enough: Recovering Scene Content From Camera Poses
• NeurIPS 2026Asymmetric Invertible Threat: Learning Reversible Privacy Defense for Face Recognition
• ICML 2026Accurate, Private, Secure, Federated U-statistics with Higher Degree
• ICLR 2026Measuring Physical-World Privacy Awareness of Large Language Models: An Evaluation Benchmark
• CVPR 2026LDP-Slicing Local Differential Privacy for Images via Randomized Bit-Plane Slicing

全部社会层面 · privacy论文

共 87 篇，第 2 / 2 页

51. NeurIPS 2024On Differentially Private U Statistics
52. NeurIPS 2024On provable privacy vulnerabilities of graph representations
53. NeurIPS 2024On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
54. NeurIPS 2024On the Computational Complexity of Private High-dimensional Model Selection
55. NeurIPS 2024Oracle-Efficient Differentially Private Learning with Public Data
56. NeurIPS 2024OSLO: One-Shot Label-Only Membership Inference Attacks
57. NeurIPS 2024PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
58. NeurIPS 2024Pre-training Differentially Private Models with Limited Public Data
59. NeurIPS 2024Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
60. NeurIPS 2024Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
61. NeurIPS 2024Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
62. NeurIPS 2024PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
63. NeurIPS 2024Private and Personalized Frequency Estimation in a Federated Setting
64. NeurIPS 2024Private Attribute Inference from Images with Vision-Language Models
65. NeurIPS 2024Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust
66. NeurIPS 2024Private Geometric Median
67. NeurIPS 2024Private Online Learning via Lazy Algorithms
68. NeurIPS 2024Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
69. NeurIPS 2024PrivCirNet: Efficient Private Inference via Block Circulant Transformation
70. NeurIPS 2024Pseudo-Private Data Guided Model Inversion Attacks
71. NeurIPS 2024Public-data Assisted Private Stochastic Optimization: Power and Limitations
72. NeurIPS 2024RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees
73. NeurIPS 2024Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
74. NeurIPS 2024Revisiting Differentially Private ReLU Regression
75. NeurIPS 2024RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation
76. NeurIPS 2024Sample-Efficient Private Learning of Mixtures of Gaussians
77. NeurIPS 2024Scalable DP-SGD: Shuffling vs. Poisson Subsampling
78. NeurIPS 2024Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models
79. NeurIPS 2024SPEAR: Exact Gradient Inversion of Batches in Federated Learning
80. NeurIPS 2024SuperDeepFool: a new fast and accurate minimal adversarial attack
81. NeurIPS 2024The Limits of Differential Privacy in Online Learning
82. NeurIPS 2024Trap-MID: Trapdoor-based Defense against Model Inversion Attacks
83. NeurIPS 2024Truthful High Dimensional Sparse Linear Regression
84. NeurIPS 2024Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
85. NeurIPS 2024Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification
86. NeurIPS 2024United We Stand, Divided We Fall: Fingerprinting Deep Neural Networks via Adversarial Trajectories
87. NeurIPS 2024Universal Exact Compression of Differentially Private Mechanisms

常见问题

2023–2026 年四大会议一共有多少篇社会层面 · privacy论文？

共 431 篇，按年份分别是 2023 年 21 篇、2024 年 117 篇、2025 年 155 篇、2026 年 138 篇。

NeurIPS 2026 有多少篇社会层面 · privacy论文？

NeurIPS 2026 有 44 篇社会层面 · privacy论文，上一届是 80 篇。

ICLR 2026 有多少篇社会层面 · privacy论文？

ICLR 2026 有 2 篇社会层面 · privacy论文，上一届是 2 篇。

ICML 2026 有多少篇社会层面 · privacy论文？

ICML 2026 有 90 篇社会层面 · privacy论文，上一届是 71 篇。

CVPR 2026 有多少篇社会层面 · privacy论文？

CVPR 2026 有 2 篇社会层面 · privacy论文，上一届是 2 篇。

哪个会议的社会层面 · privacy论文最多？

NeurIPS，2023–2026 年共 215 篇。