{"total":36,"rows":[{"id":"2310.16789","title":"Detecting Pretraining Data from Large Language Models","created":"2023-10-25","cited_by_count":387,"area":null},{"id":"2202.06539","title":"Deduplicating Training Data Mitigates Privacy Risks in Language Models","created":"2022-02-14","cited_by_count":314,"area":null},{"id":"2206.00719","title":"Dataset Distillation using Neural Feature Regression","created":"2022-06-01","cited_by_count":177,"area":null},{"id":"2206.07758","title":"Reconstructing Training Data from Trained Neural Networks","created":"2022-06-15","cited_by_count":133,"area":null},{"id":"2301.05603","title":"A Comprehensive Survey of Dataset Distillation","created":"2023-01-13","cited_by_count":115,"area":null},{"id":"2402.09910","title":"DE-COP: Detecting Copyrighted Content in Language Models Training Data","created":"2024-02-15","cited_by_count":74,"area":null},{"id":"2203.01925","title":"Label-Only Model Inversion Attacks via Boundary Repulsion","created":"2022-03-03","cited_by_count":56,"area":null},{"id":"2107.01614","title":"Survey: Leakage and Privacy at Inference Time","created":"2021-07-04","cited_by_count":44,"area":null},{"id":"2102.11673","title":"Measuring Data Leakage in Machine-Learning Models with Fisher Information","created":"2021-02-23","cited_by_count":33,"area":null},{"id":"2310.01424","title":"Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey","created":"2023-09-27","cited_by_count":30,"area":null},{"id":"2205.08443","title":"On the (In)security of Peer-to-Peer Decentralized Machine Learning","created":"2022-05-17","cited_by_count":23,"area":null},{"id":"2310.00339","title":"FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation","created":"2023-09-30","cited_by_count":22,"area":null},{"id":"2203.09566","title":"Leveraging Adversarial Examples to Quantify Membership Information Leakage","created":"2022-03-17","cited_by_count":19,"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":"2406.03880","title":"Memorization in deep learning: A survey","created":"2024-06-06","cited_by_count":15,"area":null},{"id":"2105.03875","title":"Bounding Information Leakage in Machine Learning","created":"2021-05-09","cited_by_count":11,"area":null},{"id":"2208.13893","title":"Data Isotopes for Data Provenance in DNNs","created":"2022-08-29","cited_by_count":11,"area":null},{"id":"2103.01496","title":"DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing","created":"2021-03-02","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":"2402.09299","title":"Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code","created":"2024-02-14","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":"2207.10802","title":"Combing for Credentials: Active Pattern Extraction from Smart Reply","created":"2022-07-14","cited_by_count":7,"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":"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":"2310.00488","title":"Membership Privacy Risks of Sharpness Aware Minimization","created":"2023-09-30","cited_by_count":2,"area":null},{"id":"2310.10124","title":"A Comprehensive Study of Privacy Risks in Curriculum Learning","created":"2023-10-16","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":"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":"2209.01538","title":"Data Provenance via Differential Auditing","created":"2022-09-04","cited_by_count":1,"area":null},{"id":"2404.00696","title":"Privacy Re-identification Attacks on Tabular GANs","created":"2024-03-31","cited_by_count":1,"area":null},{"id":"2506.23296","title":"Securing AI Systems: A Guide to Known Attacks and Impacts","created":"2025-06-29","cited_by_count":1,"area":null},{"id":"2310.20299","title":"Verification of Neural Networks Local Differential Classification Privacy","created":"2023-10-31","cited_by_count":0,"area":null},{"id":"2401.11723","title":"Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them","created":"2024-01-22","cited_by_count":0,"area":null},{"id":"2406.00249","title":"Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning","created":"2024-06-01","cited_by_count":0,"area":null},{"id":"2503.04231","title":"One-Shot Clustering for Federated Learning","created":"2025-03-06","cited_by_count":0,"area":null},{"id":"2509.01587","title":"One-Shot Clustering for Federated Learning Under Clustering-Agnostic Assumption","created":"2025-09-01","cited_by_count":0,"area":null}],"next_cursor":null}