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

215 papers, page 1 of 5

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
45. NeurIPS 2025A Generalized Binary Tree Mechanism for Private Approximation of All-Pair Shortest Distances
46. NeurIPS 2025A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input
47. NeurIPS 2025A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation
48. NeurIPS 2025Adaptive Data Analysis for Growing Data
49. NeurIPS 2025Adaptive Sigmoid Clipping for Balancing the Direction–Magnitude Mismatch Trade-off in Differentially Private Learning
50. NeurIPS 2025AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents

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.