{"total":18,"rows":[{"id":"2410.22235","title":"Auditing $f$-Differential Privacy in One Run","created":"2024-10-29","cited_by_count":30,"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":"2409.03363","title":"Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding","created":"2024-09-05","cited_by_count":20,"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":"2411.10023","title":"Model Inversion Attacks: A Survey of Approaches and Countermeasures","created":"2024-11-15","cited_by_count":11,"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":"2503.20846","title":"Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead","created":"2025-03-26","cited_by_count":4,"area":null},{"id":"2511.06064","title":"A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data","created":"2025-11-08","cited_by_count":2,"area":null},{"id":"2407.10446","title":"DDFAD: Dataset Distillation Framework for Audio Data","created":"2024-07-15","cited_by_count":1,"area":null},{"id":"2411.16763","title":"Hide in Plain Sight: Clean-Label Backdoor for Auditing Membership Inference","created":"2024-11-24","cited_by_count":1,"area":null},{"id":"2510.09717","title":"Provable Training Data Identification for Large Language Models","created":"2025-10-10","cited_by_count":1,"area":null},{"id":"2307.13771","title":"Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach","created":"2023-07-25","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":"2412.05842","title":"DREAM: Domain-agnostic Reverse Engineering Attributes of Black-box Model","created":"2024-12-08","cited_by_count":0,"area":null},{"id":"2511.14084","title":"Observational Auditing of Label Privacy","created":"2025-11-18","cited_by_count":0,"area":null},{"id":"2512.17075","title":"Perturb Your Data: Paraphrase-Guided Training Data Watermarking","created":"2025-12-18","cited_by_count":0,"area":null},{"id":"2601.06827","title":"PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection","created":"2026-01-11","cited_by_count":0,"area":null},{"id":"2606.31991","title":"Anchored or Drifting: What Recursive Self-Generation Reveals About Training Data","created":"2026-06-30","cited_by_count":0,"area":null}],"next_cursor":null}