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

117 papers, page 1 of 3

1. CVPR 2024DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection
2. CVPR 2024Efficient Privacy-Preserving Visual Localization Using 3D Ray Clouds
3. CVPR 2024FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
4. CVPR 2024Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
5. CVPR 2024Privacy-Preserving Optics for Enhancing Protection in Face De-Identification
6. CVPR 2024Validating Privacy-Preserving Face Recognition under a Minimum Assumption
7. ICLR 2024Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
8. ICLR 2024Beyond Memorization: Violating Privacy via Inference with Large Language Models
9. ICLR 2024Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
10. ICLR 2024DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
11. ICLR 2024FedInverse: Evaluating Privacy Leakage in Federated Learning
12. ICLR 2024Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
13. ICLR 2024LabelDP-Pro: Learning with Label Differential Privacy via Projections
14. ICLR 2024Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
15. ICLR 2024Privacy Amplification for Matrix Mechanisms
16. ICLR 2024Privacy-Preserving In-Context Learning for Large Language Models
17. ICLR 2024Unprocessing Seven Years of Algorithmic Fairness
18. ICML 2024Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy
19. ICML 2024Mean Estimation in the Add-Remove Model of Differential Privacy
20. ICML 2024Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
21. ICML 2024Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning
22. ICML 2024Privacy Attacks in Decentralized Learning
23. ICML 2024Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
24. ICML 2024Privacy Preserving Adaptive Experiment Design
25. ICML 2024Privacy Profiles for Private Selection
26. ICML 2024Privacy-Preserving Embedding via Look-up Table Evaluation with Fully Homomorphic Encryption
27. ICML 2024Provable Privacy with Non-Private Pre-Processing
28. ICML 2024Shifted Interpolation for Differential Privacy
29. ICML 2024The Privacy Power of Correlated Noise in Decentralized Learning
30. ICML 2024Transferable Facial Privacy Protection against Blind Face Restoration via Domain-Consistent Adversarial Obfuscation
31. NeurIPS 2024A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
32. NeurIPS 2024A Synthetic Dataset for Personal Attribute Inference
33. NeurIPS 2024Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
34. NeurIPS 2024Attack-Aware Noise Calibration for Differential Privacy
35. NeurIPS 2024Auditing Privacy Mechanisms via Label Inference Attacks
36. NeurIPS 2024Banded Square Root Matrix Factorization for Differentially Private Model Training
37. NeurIPS 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
38. NeurIPS 2024Certified Machine Unlearning via Noisy Stochastic Gradient Descent
39. NeurIPS 2024Continual Counting with Gradual Privacy Expiration
40. NeurIPS 2024Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature
41. NeurIPS 2024DAGER: Exact Gradient Inversion for Large Language Models
42. NeurIPS 2024DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans
43. NeurIPS 2024Déjà Vu Memorization in Vision–Language Models
44. NeurIPS 2024Differential Privacy in Scalable General Kernel Learning via $K$-means Nystr{\"o}m Random Features
45. NeurIPS 2024Differentially Private Equivalence Testing for Continuous Distributions and Applications
46. NeurIPS 2024Differentially Private Optimization with Sparse Gradients
47. NeurIPS 2024Differentially Private Reinforcement Learning with Self-Play
48. NeurIPS 2024Differentially Private Set Representations
49. NeurIPS 2024Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
50. NeurIPS 2024Dimension-free Private Mean Estimation for Anisotropic Distributions

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.