{"total":124,"rows":[{"id":"2112.12938","title":"Counterfactual Memorization in Neural Language Models","created":"2021-12-24","cited_by_count":156,"area":null},{"id":"2303.00654","title":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","created":"2023-03-01","cited_by_count":153,"area":null},{"id":"2206.10469","title":"The Privacy Onion Effect: Memorization is Relative","created":"2022-06-21","cited_by_count":136,"area":null},{"id":"2206.00240","title":"Privacy for Free: How does Dataset Condensation Help Privacy?","created":"2022-06-01","cited_by_count":120,"area":null},{"id":"2207.00099","title":"Measuring Forgetting of Memorized Training Examples","created":"2022-06-30","cited_by_count":118,"area":null},{"id":"2204.00032","title":"Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets","created":"2022-03-31","cited_by_count":101,"area":null},{"id":"2104.14380","title":"PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments","created":"2021-04-29","cited_by_count":93,"area":null},{"id":"2106.09352","title":"Large Scale Private Learning via Low-rank Reparametrization","created":"2021-06-17","cited_by_count":88,"area":null},{"id":"2102.12677","title":"Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning","created":"2021-02-25","cited_by_count":86,"area":null},{"id":"2106.08320","title":"Self-Supervised Learning with Kernel Dependence Maximization","created":"2021-06-15","cited_by_count":77,"area":null},{"id":"2302.04460","title":"Bag of Tricks for Training Data Extraction from Language Models","created":"2023-02-09","cited_by_count":70,"area":null},{"id":"2505.24832","title":"How much do language models memorize?","created":"2025-05-30","cited_by_count":61,"area":null},{"id":"2101.11073","title":"Property Inference From Poisoning","created":"2021-01-26","cited_by_count":60,"area":null},{"id":"2409.14781","title":"Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method","created":"2024-09-23","cited_by_count":55,"area":null},{"id":"2203.11481","title":"Mixed Differential Privacy in Computer Vision","created":"2022-03-22","cited_by_count":54,"area":null},{"id":"2212.10986","title":"SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning","created":"2022-12-21","cited_by_count":51,"area":null},{"id":"2112.08331","title":"Model Stealing Attacks Against Inductive Graph Neural Networks","created":"2021-12-15","cited_by_count":50,"area":null},{"id":"2107.01614","title":"Survey: Leakage and Privacy at Inference Time","created":"2021-07-04","cited_by_count":44,"area":null},{"id":"2302.13406","title":"GNNDelete: A General Strategy for Unlearning in Graph Neural Networks","created":"2023-02-26","cited_by_count":43,"area":null},{"id":"2402.09363","title":"Copyright Traps for Large Language Models","created":"2024-02-14","cited_by_count":37,"area":null},{"id":"2310.09266","title":"User Inference Attacks on Large Language Models","created":"2023-10-13","cited_by_count":36,"area":null},{"id":"2404.17399","title":"Evaluations of Machine Learning Privacy Defenses are Misleading","created":"2024-04-26","cited_by_count":36,"area":null},{"id":"2202.07623","title":"Defending against Reconstruction Attacks with R\\'enyi Differential Privacy","created":"2022-02-15","cited_by_count":32,"area":null},{"id":"2201.07513","title":"Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders","created":"2022-01-19","cited_by_count":31,"area":null},{"id":"2208.12348","title":"SNAP: Efficient Extraction of Private Properties with Poisoning","created":"2022-08-25","cited_by_count":31,"area":null},{"id":"2404.01231","title":"Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models","created":"2024-04-01","cited_by_count":30,"area":null},{"id":"2410.22235","title":"Auditing $f$-Differential Privacy in One Run","created":"2024-10-29","cited_by_count":30,"area":null},{"id":"2107.06018","title":"This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces","created":"2021-07-13","cited_by_count":25,"area":null},{"id":"2310.14369","title":"MoPe: Model Perturbation-based Privacy Attacks on Language Models","created":"2023-10-22","cited_by_count":25,"area":null},{"id":"2406.01333","title":"Probing Language Models for Pre-training Data Detection","created":"2024-06-03","cited_by_count":25,"area":null},{"id":"2210.02912","title":"CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning","created":"2022-10-06","cited_by_count":24,"area":null},{"id":"2209.14987","title":"No Free Lunch in \"Privacy for Free: How does Dataset Condensation Help Privacy\"","created":"2022-09-29","cited_by_count":23,"area":null},{"id":"2306.16064","title":"Federated Generative Learning with Foundation Models","created":"2023-06-28","cited_by_count":23,"area":null},{"id":"2307.10655","title":"A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency","created":"2023-07-20","cited_by_count":21,"area":null},{"id":"2405.14106","title":"Nearly Tight Black-Box Auditing of Differentially Private Machine Learning","created":"2024-05-23","cited_by_count":21,"area":null},{"id":"2503.17514","title":"Language Models May Verbatim Complete Text They Were Not Explicitly Trained On","created":"2025-03-21","cited_by_count":21,"area":null},{"id":"2206.02617","title":"Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent","created":"2022-06-06","cited_by_count":20,"area":null},{"id":"2307.16273","title":"zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training","created":"2023-07-30","cited_by_count":20,"area":null},{"id":"2304.13850","title":"Do SSL Models Have D\\'ej\\`a Vu? A Case of Unintended Memorization in Self-supervised Learning","created":"2023-04-26","cited_by_count":19,"area":null},{"id":"2210.13662","title":"Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano","created":"2022-10-24","cited_by_count":17,"area":null},{"id":"2208.08270","title":"On the Privacy Effect of Data Enhancement via the Lens of Memorization","created":"2022-08-17","cited_by_count":15,"area":null},{"id":"2208.10134","title":"Machine Learning with Confidential Computing: A Systematization of Knowledge","created":"2022-08-22","cited_by_count":15,"area":null},{"id":"2410.16618","title":"SoK: Dataset Copyright Auditing in Machine Learning Systems","created":"2024-10-22","cited_by_count":15,"area":null},{"id":"2306.01181","title":"TMI! Finetuned Models Leak Private Information from their Pretraining Data","created":"2023-06-01","cited_by_count":14,"area":null},{"id":"2406.13012","title":"Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models","created":"2024-06-18","cited_by_count":12,"area":null},{"id":"2105.03875","title":"Bounding Information Leakage in Machine Learning","created":"2021-05-09","cited_by_count":11,"area":null},{"id":"2410.06186","title":"The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD","created":"2024-10-08","cited_by_count":11,"area":null},{"id":"2109.10870","title":"SoK: Machine Learning Governance","created":"2021-09-20","cited_by_count":10,"area":null},{"id":"2206.04591","title":"Privacy Leakage in Text Classification: A Data Extraction Approach","created":"2022-06-09","cited_by_count":10,"area":null},{"id":"2502.15680","title":"Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training","created":"2025-02-21","cited_by_count":10,"area":null},{"id":"2204.12822","title":"A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions","created":"2022-04-27","cited_by_count":9,"area":null},{"id":"2306.05093","title":"Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting","created":"2023-06-08","cited_by_count":9,"area":null},{"id":"2504.02199","title":"ESC: Erasing Space Concept for Knowledge Deletion","created":"2025-04-03","cited_by_count":9,"area":null},{"id":"2206.14004","title":"On the amplification of security and privacy risks by post-hoc explanations in machine learning models","created":"2022-06-28","cited_by_count":8,"area":null},{"id":"2208.05969","title":"Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment","created":"2022-08-11","cited_by_count":8,"area":null},{"id":"2210.11049","title":"How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers","created":"2022-10-20","cited_by_count":8,"area":null},{"id":"2403.14772","title":"Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures","created":"2024-03-21","cited_by_count":8,"area":null},{"id":"2501.03941","title":"Synthetic Data Privacy Metrics","created":"2025-01-07","cited_by_count":8,"area":null},{"id":"2101.05405","title":"Training Data Leakage Analysis in Language Models","created":"2021-01-14","cited_by_count":7,"area":null},{"id":"2205.11090","title":"FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders","created":"2022-05-23","cited_by_count":7,"area":null},{"id":"2207.10802","title":"Combing for Credentials: Active Pattern Extraction from Smart Reply","created":"2022-07-14","cited_by_count":7,"area":null},{"id":"2401.06031","title":"GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model","created":"2024-01-11","cited_by_count":7,"area":null},{"id":"2101.02997","title":"Differentially Private Federated Learning for Cancer Prediction","created":"2021-01-08","cited_by_count":6,"area":null},{"id":"2202.11655","title":"TEE-based decentralized recommender systems: The raw data sharing redemption","created":"2022-02-23","cited_by_count":6,"area":null},{"id":"2209.06529","title":"Data Privacy and Trustworthy Machine Learning","created":"2022-09-14","cited_by_count":6,"area":null},{"id":"2306.13789","title":"Deconstructing Classifiers: Towards A Data Reconstruction Attack Against Text Classification Models","created":"2023-06-23","cited_by_count":6,"area":null},{"id":"2404.00673","title":"A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures","created":"2024-03-31","cited_by_count":6,"area":null},{"id":"2503.07501","title":"Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond","created":"2025-03-10","cited_by_count":6,"area":null},{"id":"2503.04963","title":"Energy-Latency Attacks: A New Adversarial Threat to Deep Learning","created":"2025-03-06","cited_by_count":5,"area":null},{"id":"2508.21146","title":"Privacy Auditing Synthetic Data Release through Local Likelihood Attacks","created":"2025-08-28","cited_by_count":5,"area":null},{"id":"2105.01401","title":"A Review of Confidentiality Threats Against Embedded Neural Network Models","created":"2021-05-04","cited_by_count":4,"area":null},{"id":"2109.09023","title":"Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks","created":"2021-09-18","cited_by_count":4,"area":null},{"id":"2205.06506","title":"Collaborative Drug Discovery: Inference-level Data Protection Perspective","created":"2022-05-13","cited_by_count":4,"area":null},{"id":"2403.01268","title":"Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach","created":"2024-03-02","cited_by_count":4,"area":null},{"id":"2405.19272","title":"Differentially Private Clustered Federated Learning","created":"2024-05-29","cited_by_count":4,"area":null},{"id":"2408.16913","title":"Analyzing Inference Privacy Risks Through Gradients in Machine Learning","created":"2024-08-29","cited_by_count":4,"area":null},{"id":"2409.06280","title":"Anonymity Unveiled: A Practical Framework for Auditing Data Use in Deep Learning Models","created":"2024-09-10","cited_by_count":4,"area":null},{"id":"2501.10985","title":"GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models","created":"2025-01-19","cited_by_count":4,"area":null},{"id":"2506.04681","title":"Urania: Differentially Private Insights into AI Use","created":"2025-06-05","cited_by_count":4,"area":null},{"id":"2509.18014","title":"Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis","created":"2025-09-22","cited_by_count":4,"area":null},{"id":"2205.13941","title":"Auditing Differential Privacy in High Dimensions with the Kernel Quantum R\\'enyi Divergence","created":"2022-05-27","cited_by_count":3,"area":null},{"id":"2210.11237","title":"Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey","created":"2022-10-19","cited_by_count":3,"area":null},{"id":"2305.12997","title":"Evaluating Privacy Leakage in Split Learning","created":"2023-05-22","cited_by_count":3,"area":null},{"id":"2312.05807","title":"Federated Learning Empowered by Generative Content","created":"2023-12-10","cited_by_count":3,"area":null},{"id":"2406.15789","title":"Privacy Implications of Explainable AI in Data-Driven Systems","created":"2024-06-22","cited_by_count":3,"area":null},{"id":"2409.17538","title":"LoRA Provides Differential Privacy by Design via Random Sketching","created":"2024-09-26","cited_by_count":3,"area":null},{"id":"2409.19413","title":"Membership Privacy Evaluation in Deep Spiking Neural Networks","created":"2024-09-28","cited_by_count":3,"area":null},{"id":"2211.13416","title":"Data Origin Inference in Machine Learning","created":"2022-11-24","cited_by_count":2,"area":null},{"id":"2310.00488","title":"Membership Privacy Risks of Sharpness Aware Minimization","created":"2023-09-30","cited_by_count":2,"area":null},{"id":"2402.12235","title":"The Fundamental Limits of Least-Privilege Learning","created":"2024-02-19","cited_by_count":2,"area":null},{"id":"2405.20620","title":"\"Forgetting\" in Machine Learning and Beyond: A Survey","created":"2024-05-31","cited_by_count":2,"area":null},{"id":"2406.02027","title":"Inference Attacks: A Taxonomy, Survey, and Promising Directions","created":"2024-06-04","cited_by_count":2,"area":null},{"id":"2503.06150","title":"Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers","created":"2025-03-08","cited_by_count":2,"area":null},{"id":"2601.04301","title":"Quantifying the Effect of Test Set Contamination on Generative Evaluations","created":"2026-01-07","cited_by_count":2,"area":null},{"id":"2110.03054","title":"On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models","created":"2021-10-06","cited_by_count":1,"area":null},{"id":"2203.16701","title":"Towards Differential Relational Privacy and its use in Question Answering","created":"2022-03-30","cited_by_count":1,"area":null},{"id":"2210.04840","title":"Rieoptax: Riemannian Optimization in JAX","created":"2022-10-10","cited_by_count":1,"area":null},{"id":"2307.05194","title":"Differentially Private Statistical Inference through $\\beta$-Divergence One Posterior Sampling","created":"2023-07-11","cited_by_count":1,"area":null},{"id":"2311.03075","title":"SoK: Memorisation in machine learning","created":"2023-11-06","cited_by_count":1,"area":null},{"id":"2404.02696","title":"Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition","created":"2024-04-03","cited_by_count":1,"area":null}],"next_cursor":"100"}