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

431 papers, page 8 of 9

351. NeurIPS 2024Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
352. NeurIPS 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
353. NeurIPS 2024Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
354. NeurIPS 2024FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection
355. NeurIPS 2024FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
356. NeurIPS 2024GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
357. NeurIPS 2024Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images
358. NeurIPS 2024GS-Hider: Hiding Messages into 3D Gaussian Splatting
359. NeurIPS 2024HEPrune: Fast Private Training of Deep Neural Networks With Encrypted Data Pruning
360. NeurIPS 2024Instance-Optimal Private Density Estimation in the Wasserstein Distance
361. NeurIPS 2024Instance-Specific Asymmetric Sensitivity in Differential Privacy
362. NeurIPS 2024Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
363. NeurIPS 2024Large Language Model Unlearning
364. NeurIPS 2024Measuring Dejavu Memorization Efficiently
365. NeurIPS 2024Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
366. NeurIPS 2024Membership Inference Attacks against Large Vision-Language Models
367. NeurIPS 2024Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy
368. NeurIPS 2024Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
369. NeurIPS 2024Nimbus: Secure and Efficient Two-Party Inference for Transformers
370. NeurIPS 2024No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices
371. NeurIPS 2024Noise-Aware Differentially Private Regression via Meta-Learning
372. NeurIPS 2024Noisy Dual Mirror Descent: A Near Optimal Algorithm for Jointly-DP Convex Resource Allocation
373. NeurIPS 2024On Differentially Private Subspace Estimation in a Distribution-Free Setting
374. NeurIPS 2024On Differentially Private U Statistics
375. NeurIPS 2024On provable privacy vulnerabilities of graph representations
376. NeurIPS 2024On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
377. NeurIPS 2024On the Computational Complexity of Private High-dimensional Model Selection
378. NeurIPS 2024Oracle-Efficient Differentially Private Learning with Public Data
379. NeurIPS 2024OSLO: One-Shot Label-Only Membership Inference Attacks
380. NeurIPS 2024PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
381. NeurIPS 2024Pre-training Differentially Private Models with Limited Public Data
382. NeurIPS 2024Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
383. NeurIPS 2024Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
384. NeurIPS 2024Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
385. NeurIPS 2024PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
386. NeurIPS 2024Private and Personalized Frequency Estimation in a Federated Setting
387. NeurIPS 2024Private Attribute Inference from Images with Vision-Language Models
388. NeurIPS 2024Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust
389. NeurIPS 2024Private Geometric Median
390. NeurIPS 2024Private Online Learning via Lazy Algorithms
391. NeurIPS 2024Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
392. NeurIPS 2024PrivCirNet: Efficient Private Inference via Block Circulant Transformation
393. NeurIPS 2024Pseudo-Private Data Guided Model Inversion Attacks
394. NeurIPS 2024Public-data Assisted Private Stochastic Optimization: Power and Limitations
395. NeurIPS 2024RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees
396. NeurIPS 2024Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
397. NeurIPS 2024Revisiting Differentially Private ReLU Regression
398. NeurIPS 2024RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation
399. NeurIPS 2024Sample-Efficient Private Learning of Mixtures of Gaussians
400. NeurIPS 2024Scalable DP-SGD: Shuffling vs. Poisson Subsampling

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.