Privacy Attacks papers, 2023–2026

Key findings

• Privacy Attacks went from 124 papers in 2023 to 219 in 2026, roughly 1.8x in three years.
• ICLR publishes the most Privacy Attacks work: 262 papers from 2023 to 2026, 38% of the four-venue total.
• The fastest-growing overlap is with Alignment and Safety: papers tagged with both rose from 2 in 2023 to 80 in 2026.
• Among the 5 topics under Federated and Privacy, it ranks #1 by 2026 paper count; the fastest grower in the group, Secure Aggregation, is up 1.8x versus 1.8x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Private Empirical Defense Against Privacy Audits
• ICML 2026A Statistical Framework for Analyzing Specification Resistance to Learnware-Inversion Risks
• ICLR 2026(Token-Level) \textbf{InfoRMIA}: Stronger Membership Inference and Privacy Assessment for LLMs
• CVPR 2026Out of Sight Out of Track Adversarial Attacks on Propagation-based
• NeurIPS 2025A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation
• ICML 2025A Certified Unlearning Approach without Access to Source Data
• ICLR 2025Adversarial Attacks on Data Attribution
• CVPR 2025Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attacks
• NeurIPS 2024Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers
• ICML 2024Collective Certified Robustness against Graph Injection Attacks
• ICLR 2024Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
• CVPR 2024Efficient Privacy-Preserving Visual Localization Using 3D Ray Clouds
• NeurIPS 2023Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
• ICML 2023Data Poisoning Attacks Against Multimodal Encoders
• ICLR 2023Adversarial Attacks on Adversarial Bandits
• CVPR 2023Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders
• NeurIPS 2026Adaptive Target-Charging with Privacy Filters and Individual Accounting
• ICML 2026How does Bayesian Sampling help Membership Inference Attacks?
• ICLR 2026Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy
• CVPR 2026Revisiting Geometric Obfuscation with Dual Convergent Lines for Privacy-Preserving Image

All Privacy Attacks papers

209 papers, page 4 of 5

151. NeurIPS 2024SPEAR: Exact Gradient Inversion of Batches in Federated Learning
152. NeurIPS 2024Stealth edits to large language models
153. NeurIPS 2024SuperDeepFool: a new fast and accurate minimal adversarial attack
154. NeurIPS 2024TARP-VP: Towards Evaluation of Transferred Adversarial Robustness and Privacy on Label Mapping Visual Prompting Models
155. NeurIPS 2024The Limits of Differential Privacy in Online Learning
156. NeurIPS 2024Trap-MID: Trapdoor-based Defense against Model Inversion Attacks
157. NeurIPS 2024Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
158. NeurIPS 2024Unelicitable Backdoors via Cryptographic Transformer Circuits
159. NeurIPS 2024United We Stand, Divided We Fall: Fingerprinting Deep Neural Networks via Adversarial Trajectories
160. NeurIPS 2024Watermarking Makes Language Models Radioactive
161. NeurIPS 2024WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks
162. NeurIPS 2024Wide Two-Layer Networks can Learn from Adversarial Perturbations
163. NeurIPS 2024WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models
164. NeurIPS 2023$k$-Means Clustering with Distance-Based Privacy
165. NeurIPS 2023A Unified Detection Framework for Inference-Stage Backdoor Defenses
166. NeurIPS 2023A Unified Fast Gradient Clipping Framework for DP-SGD
167. NeurIPS 2023Adaptive Privacy Composition for Accuracy-first Mechanisms
168. NeurIPS 2023Adversarially Robust Distributed Count Tracking via Partial Differential Privacy
169. NeurIPS 2023Batchnorm Allows Unsupervised Radial Attacks
170. NeurIPS 2023Blurred-Dilated Method for Adversarial Attacks
171. NeurIPS 2023Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
172. NeurIPS 2023Bounding training data reconstruction in DP-SGD
173. NeurIPS 2023Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
174. NeurIPS 2023Counting Distinct Elements Under Person-Level Differential Privacy
175. NeurIPS 2023Data Minimization at Inference Time
176. NeurIPS 2023DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
177. NeurIPS 2023Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling
178. NeurIPS 2023Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
179. NeurIPS 2023Faster approximate subgraph counts with privacy
180. NeurIPS 2023FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning
181. NeurIPS 2023Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
182. NeurIPS 2023GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference
183. NeurIPS 2023Gaussian Differential Privacy on Riemannian Manifolds
184. NeurIPS 2023Gaussian Membership Inference Privacy
185. NeurIPS 2023Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
186. NeurIPS 2023k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
187. NeurIPS 2023Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency
188. NeurIPS 2023Label-Only Model Inversion Attacks via Knowledge Transfer
189. NeurIPS 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
190. NeurIPS 2023Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack
191. NeurIPS 2023Minimax Risks and Optimal Procedures for Estimation under Functional Local Differential Privacy
192. NeurIPS 2023On Computing Pairwise Statistics with Local Differential Privacy
193. NeurIPS 2023On Private and Robust Bandits
194. NeurIPS 2023Optimal Unbiased Randomizers for Regression with Label Differential Privacy
195. NeurIPS 2023Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
196. NeurIPS 2023Practical Differentially Private Hyperparameter Tuning with Subsampling
197. NeurIPS 2023Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
198. NeurIPS 2023Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?
199. NeurIPS 2023Privacy Auditing with One (1) Training Run
200. NeurIPS 2023Private Federated Frequency Estimation: Adapting to the Hardness of the Instance

FAQ

How many Privacy Attacks papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

698 in total: 124 in 2023, 180 in 2024, 175 in 2025, 219 in 2026.

How many Privacy Attacks papers are at NeurIPS 2026?

NeurIPS 2026 has 55 Privacy Attacks papers, versus 45 the year before.

How many Privacy Attacks papers are at ICLR 2026?

ICLR 2026 has 102 Privacy Attacks papers, versus 83 the year before.

How many Privacy Attacks papers are at ICML 2026?

ICML 2026 has 38 Privacy Attacks papers, versus 29 the year before.

How many Privacy Attacks papers are at CVPR 2026?

CVPR 2026 has 24 Privacy Attacks papers, versus 18 the year before.

Which conference publishes the most Privacy Attacks papers?

ICLR, with 262 papers from 2023 to 2026.