{"total":23,"rows":[{"id":"2101.04535","title":"Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning","created":"2021-01-11","cited_by_count":199,"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":"2307.02106","title":"SoK: Privacy-Preserving Data Synthesis","created":"2023-07-05","cited_by_count":38,"area":null},{"id":"2302.01622","title":"Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging","created":"2023-02-03","cited_by_count":17,"area":null},{"id":"2507.06969","title":"Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy","created":"2025-07-09","cited_by_count":15,"area":null},{"id":"2312.05114","title":"The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against \"Truly Anonymous\" Synthetic Datasets","created":"2023-12-08","cited_by_count":14,"area":null},{"id":"2111.05791","title":"Distribution-Invariant Differential Privacy","created":"2021-11-08","cited_by_count":6,"area":null},{"id":"2304.07210","title":"Measuring Re-identification Risk","created":"2023-04-12","cited_by_count":6,"area":null},{"id":"2210.12526","title":"Federated Calibration and Evaluation of Binary Classifiers","created":"2022-10-22","cited_by_count":5,"area":null},{"id":"2307.01875","title":"Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning","created":"2023-07-04","cited_by_count":5,"area":null},{"id":"2303.16704","title":"TraVaG: Differentially Private Trace Variant Generation Using GANs","created":"2023-03-29","cited_by_count":3,"area":null},{"id":"2307.00359","title":"When Synthetic Data Met Regulation","created":"2023-07-01","cited_by_count":3,"area":null},{"id":"2311.10500","title":"From Principle to Practice: Vertical Data Minimization for Machine Learning","created":"2023-11-17","cited_by_count":3,"area":null},{"id":"2407.12669","title":"Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data","created":"2024-07-17","cited_by_count":3,"area":null},{"id":"2312.04590","title":"Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging","created":"2023-12-05","cited_by_count":1,"area":null},{"id":"2505.13085","title":"Universal Semantic Disentangled Privacy-preserving Speech Representation Learning","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2601.22434","title":"Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2210.00884","title":"Generating Synthetic Data with Locally Estimated Distributions for Disclosure Control","created":"2022-10-03","cited_by_count":0,"area":null},{"id":"2408.04478","title":"NFDI4Health workflow and service for synthetic data generation, assessment and risk management","created":"2024-08-08","cited_by_count":0,"area":null},{"id":"2504.06418","title":"Releasing Differentially Private Event Logs Using Generative Models","created":"2025-04-08","cited_by_count":0,"area":null},{"id":"2504.21199","title":"Generate-then-Verify: Reconstructing Data from Limited Published Statistics","created":"2025-04-29","cited_by_count":0,"area":null},{"id":"2602.07732","title":"Efficient Adaptive Data Analysis over Dense Distributions","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2607.13928","title":"Plausible Deniability Guarantees for Whistleblowers","created":"2026-07-15","cited_by_count":0,"area":null}],"next_cursor":null}