Social aspects · privacy papers, 2023–2026

Key findings

• Social aspects · privacy went from 21 papers in 2023 to 138 in 2026, roughly 6.6x in three years.
• NeurIPS publishes the most Social aspects · privacy work: 215 papers from 2023 to 2026, 50% 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 93 in 2026.
• Among the 5 topics under Alignment and Responsible AI, it ranks #4 by 2026 paper count; the fastest grower in the group, Social aspects · safety, is up 93x versus 6.6x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Social aspects · privacy papers

87 papers, page 1 of 2

1. NeurIPS 2024A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
2. NeurIPS 2024A Synthetic Dataset for Personal Attribute Inference
3. NeurIPS 2024Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
4. NeurIPS 2024Attack-Aware Noise Calibration for Differential Privacy
5. NeurIPS 2024Auditing Privacy Mechanisms via Label Inference Attacks
6. NeurIPS 2024Banded Square Root Matrix Factorization for Differentially Private Model Training
7. NeurIPS 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
8. NeurIPS 2024Certified Machine Unlearning via Noisy Stochastic Gradient Descent
9. NeurIPS 2024Continual Counting with Gradual Privacy Expiration
10. NeurIPS 2024Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature
11. NeurIPS 2024DAGER: Exact Gradient Inversion for Large Language Models
12. NeurIPS 2024DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans
13. NeurIPS 2024Déjà Vu Memorization in Vision–Language Models
14. NeurIPS 2024Differential Privacy in Scalable General Kernel Learning via $K$-means Nystr{\"o}m Random Features
15. NeurIPS 2024Differentially Private Equivalence Testing for Continuous Distributions and Applications
16. NeurIPS 2024Differentially Private Optimization with Sparse Gradients
17. NeurIPS 2024Differentially Private Reinforcement Learning with Self-Play
18. NeurIPS 2024Differentially Private Set Representations
19. NeurIPS 2024Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
20. NeurIPS 2024Dimension-free Private Mean Estimation for Anisotropic Distributions
21. NeurIPS 2024Disentangled Style Domain for Implicit $z$-Watermark Towards Copyright Protection
22. NeurIPS 2024DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
23. NeurIPS 2024Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
24. NeurIPS 2024Efficient and Private Marginal Reconstruction with Local Non-Negativity
25. NeurIPS 2024Exactly Minimax-Optimal Locally Differentially Private Sampling
26. NeurIPS 2024Faster Algorithms for User-Level Private Stochastic Convex Optimization
27. NeurIPS 2024Faster Differentially Private Top-$k$ Selection: A Joint Exponential Mechanism with Pruning
28. NeurIPS 2024Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
29. NeurIPS 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
30. NeurIPS 2024Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
31. NeurIPS 2024FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
32. NeurIPS 2024FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
33. NeurIPS 2024GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
34. NeurIPS 2024Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images
35. NeurIPS 2024GS-Hider: Hiding Messages into 3D Gaussian Splatting
36. NeurIPS 2024HEPrune: Fast Private Training of Deep Neural Networks With Encrypted Data Pruning
37. NeurIPS 2024Instance-Optimal Private Density Estimation in the Wasserstein Distance
38. NeurIPS 2024Instance-Specific Asymmetric Sensitivity in Differential Privacy
39. NeurIPS 2024Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
40. NeurIPS 2024Large Language Model Unlearning
41. NeurIPS 2024Measuring Dejavu Memorization Efficiently
42. NeurIPS 2024Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
43. NeurIPS 2024Membership Inference Attacks against Large Vision-Language Models
44. NeurIPS 2024Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy
45. NeurIPS 2024Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
46. NeurIPS 2024Nimbus: Secure and Efficient Two-Party Inference for Transformers
47. NeurIPS 2024No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices
48. NeurIPS 2024Noise-Aware Differentially Private Regression via Meta-Learning
49. NeurIPS 2024Noisy Dual Mirror Descent: A Near Optimal Algorithm for Jointly-DP Convex Resource Allocation
50. NeurIPS 2024On Differentially Private Subspace Estimation in a Distribution-Free Setting

FAQ

How many Social aspects · privacy papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

431 in total: 21 in 2023, 117 in 2024, 155 in 2025, 138 in 2026.

How many Social aspects · privacy papers are at NeurIPS 2026?

NeurIPS 2026 has 44 Social aspects · privacy papers, versus 80 the year before.

How many Social aspects · privacy papers are at ICLR 2026?

ICLR 2026 has 2 Social aspects · privacy papers, versus 2 the year before.

How many Social aspects · privacy papers are at ICML 2026?

ICML 2026 has 90 Social aspects · privacy papers, versus 71 the year before.

How many Social aspects · privacy papers are at CVPR 2026?

CVPR 2026 has 2 Social aspects · privacy papers, versus 2 the year before.

Which conference publishes the most Social aspects · privacy papers?

NeurIPS, with 215 papers from 2023 to 2026.