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

44 papers, page 1 of 1

1. NeurIPS 2026A Private Empirical Defense Against Privacy Audits
2. NeurIPS 2026Assessing Per-Sample Membership Inference Vulnerability without Retraining
3. NeurIPS 2026Asymmetric Invertible Threat: Learning Reversible Privacy Defense for Face Recognition
4. NeurIPS 2026Asymptotically Optimal Best Arm Identification with Fixed-Budget under Differential Privacy
5. NeurIPS 2026Auditing Privacy Leakage in Tabular Foundation Model Embeddings
6. NeurIPS 2026BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation
7. NeurIPS 2026Benchmarking Membership Privacy Risks in Preference-Based LLM Post-Training
8. NeurIPS 2026Causal Evaluation of Membership Inference Attacks
9. NeurIPS 2026Detecting Hidden ML Training With Zero-Overhead Telemetry
10. NeurIPS 2026DP-EGGROLL: Centered Fitness-Vector Privatization for Backprop-Free Differentially Private Optimization
11. NeurIPS 2026DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy
12. NeurIPS 2026Efficient Label Distribution Inference Attack and Defense on Classifier Weights in Federated Learning
13. NeurIPS 2026ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregation
14. NeurIPS 2026𝑓-Differential Privacy Filters: Validity and Approximate Solutions
15. NeurIPS 2026Federated Concept-Based Models: Interpretable models with distributed supervision
16. NeurIPS 2026FILOsofer: A TEE-Shielded Model Partitioning Framework based on Fisher Information-Guided LoRA Obfuscation
17. NeurIPS 2026GOLIATH: Gradient Inversion of Tabular Diffusion Models
18. NeurIPS 2026How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study
19. NeurIPS 2026Jaguar: Fast Private CNN Inference with Power-of-Two Homomorphic Arithmetic
20. NeurIPS 2026Learning the Signature of Memorization in Autoregressive Language Models
21. NeurIPS 2026Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning
22. NeurIPS 2026Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees
23. NeurIPS 2026Mitigating Memorization Where It Happens
24. NeurIPS 2026Models Designed to Forget: Machine Unlearning via Key Deletion
25. NeurIPS 2026MPCI-Bench: A Benchmark for Multimodal Pairwise Contextual Integrity Privacy Evaluation of Language Model Agents
26. NeurIPS 2026On Differentially Private Mechanisms for Linear Regression
27. NeurIPS 2026On Inherent Privacy of Posterior Sampling: A Unified R\'enyi-Divergence Framework
28. NeurIPS 2026Online Differentially Private Consistent Clustering
29. NeurIPS 2026Optimal Algorithms for Fixed-Graph Multi-Attribution Privacy
30. NeurIPS 2026Optimal Rates for Adaptive Private $k$-PCA
31. NeurIPS 2026Optimal Rates for Differentially Private Hypothesis Testing with E-values
32. NeurIPS 2026PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization
33. NeurIPS 2026Position: Life-Logging Video Streams Make the Privacy–Utility Trade-off Inevitable
34. NeurIPS 2026Privacy Amplification Persists under Unlimited Synthetic Data Release
35. NeurIPS 2026Privacy by Postprocessing the Discrete Laplace Mechanism
36. NeurIPS 2026Privacy Meets Hierarchy: Differentially Private Distributed Trilevel Learning
37. NeurIPS 2026Privacy Risk Scales with Effective Dimension in Federated Learning
38. NeurIPS 2026PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior
39. NeurIPS 2026Private Online Prediction from Experts with Small Losses
40. NeurIPS 2026Sampling-Free Privacy Accounting for Matrix Mechanisms under Random Allocation
41. NeurIPS 2026Sequential Membership Inference Attacks
42. NeurIPS 2026Sort, Partition, Randomize: Optimal Binary Hypothesis Testing under Local Differential Privacy
43. NeurIPS 2026The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation
44. NeurIPS 2026Understanding Private Evolution as Learning-Augmented Clustering

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.