{"total":122,"rows":[{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"id":"2302.01316","title":"Are Diffusion Models Vulnerable to Membership Inference Attacks?","created":"2023-02-02","cited_by_count":136,"area":"diffusion"},{"id":"2302.04062","title":"Machine Learning for Synthetic Data Generation: A Review","created":"2023-02-08","cited_by_count":120,"area":null},{"id":"2208.14933","title":"Membership Inference Attacks by Exploiting Loss Trajectory","created":"2022-08-31","cited_by_count":101,"area":"safety"},{"id":"2302.03262","title":"Membership Inference Attacks against Diffusion Models","created":"2023-02-07","cited_by_count":86,"area":"diffusion"},{"id":"2302.13861","title":"Differentially Private Diffusion Models Generate Useful Synthetic Images","created":"2023-02-27","cited_by_count":86,"area":"diffusion"},{"id":"2102.02551","title":"ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models","created":"2021-02-04","cited_by_count":85,"area":"safety"},{"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":"2210.00968","title":"Membership Inference Attacks Against Text-to-image Generation Models","created":"2022-10-03","cited_by_count":69,"area":"safety"},{"id":"2302.12580","title":"Membership Inference Attacks against Synthetic Data through Overfitting Detection","created":"2023-02-24","cited_by_count":66,"area":"safety"},{"id":"2305.18355","title":"An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization","created":"2023-05-26","cited_by_count":54,"area":"diffusion"},{"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":"2301.09956","title":"Membership Inference of Diffusion Models","created":"2023-01-24","cited_by_count":46,"area":"diffusion"},{"id":"2306.09345","title":"Evaluating Data Attribution for Text-to-Image Models","created":"2023-06-15","cited_by_count":45,"area":null},{"id":"2107.01614","title":"Survey: Leakage and Privacy at Inference Time","created":"2021-07-04","cited_by_count":44,"area":null},{"id":"2308.06405","title":"White-box Membership Inference Attacks against Diffusion Models","created":"2023-08-11","cited_by_count":42,"area":"diffusion"},{"id":"2307.02106","title":"SoK: Privacy-Preserving Data Synthesis","created":"2023-07-05","cited_by_count":38,"area":null},{"id":"2312.08207","title":"Black-box Membership Inference Attacks against Fine-tuned Diffusion Models","created":"2023-12-13","cited_by_count":35,"area":"diffusion"},{"id":"2411.02902","title":"Membership Inference Attacks against Large Vision-Language Models","created":"2024-11-05","cited_by_count":33,"area":"mllm"},{"id":"2408.03400","title":"Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey","created":"2024-08-06","cited_by_count":32,"area":"diffusion"},{"id":"2304.03722","title":"Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data","created":"2023-04-07","cited_by_count":29,"area":null},{"id":"2312.11658","title":"Traces of Memorisation in Large Language Models for Code","created":"2023-12-18","cited_by_count":29,"area":"safety"},{"id":"2301.10053","title":"A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data","created":"2023-01-24","cited_by_count":24,"area":null},{"id":"2310.19410","title":"Generated Distributions Are All You Need for Membership Inference Attacks Against Generative Models","created":"2023-10-30","cited_by_count":22,"area":"diffusion"},{"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":"2308.12143","title":"A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models","created":"2023-08-23","cited_by_count":18,"area":"diffusion"},{"id":"2502.14921","title":"The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text","created":"2025-02-19","cited_by_count":18,"area":"synth"},{"id":"2102.07762","title":"Membership Inference Attacks are Easier on Difficult Problems","created":"2021-02-15","cited_by_count":17,"area":"safety"},{"id":"2206.01349","title":"On the Privacy Properties of GAN-generated Samples","created":"2022-06-03","cited_by_count":17,"area":"safety"},{"id":"2309.07983","title":"SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems","created":"2023-09-14","cited_by_count":14,"area":"safety"},{"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":"2405.20771","title":"Towards Black-Box Membership Inference Attack for Diffusion Models","created":"2024-05-25","cited_by_count":14,"area":"diffusion"},{"id":"2305.10994","title":"Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility","created":"2023-05-18","cited_by_count":13,"area":null},{"id":"2406.13985","title":"The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging","created":"2024-06-20","cited_by_count":13,"area":null},{"id":"2108.01644","title":"The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models","created":"2021-08-03","cited_by_count":12,"area":"safety"},{"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":"2408.05131","title":"Range Membership Inference Attacks","created":"2024-08-09","cited_by_count":12,"area":"safety"},{"id":"2308.09881","title":"Generative Adversarial Networks Unlearning","created":"2023-08-19","cited_by_count":11,"area":"safety"},{"id":"2505.01524","title":"The DCR Delusion: Measuring the Privacy Risk of Synthetic Data","created":"2025-05-02","cited_by_count":11,"area":"safety"},{"id":"2302.07801","title":"Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy","created":"2023-02-15","cited_by_count":10,"area":"diffusion"},{"id":"2410.05506","title":"Privacy Vulnerabilities in Marginals-based Synthetic Data","created":"2024-10-07","cited_by_count":10,"area":null},{"id":"2202.04856","title":"PPA: Preference Profiling Attack Against Federated Learning","created":"2022-02-10","cited_by_count":9,"area":null},{"id":"2302.02910","title":"An Empirical Analysis of Fairness Notions under Differential Privacy","created":"2023-02-06","cited_by_count":9,"area":null},{"id":"2407.13252","title":"Unveiling Structural Memorization: Structural Membership Inference Attack for Text-to-Image Diffusion Models","created":"2024-07-18","cited_by_count":9,"area":"diffusion"},{"id":"2506.06108","title":"Synthetic Tabular Data: Methods, Attacks and Defenses","created":"2025-06-06","cited_by_count":9,"area":null},{"id":"2101.02069","title":"Model Extraction and Defenses on Generative Adversarial Networks","created":"2021-01-06","cited_by_count":8,"area":null},{"id":"2102.03314","title":"On Utility and Privacy in Synthetic Genomic Data","created":"2021-02-05","cited_by_count":8,"area":"safety"},{"id":"2203.01363","title":"Faking feature importance: A cautionary tale on the use of differentially-private synthetic data","created":"2022-03-02","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":"2406.19395","title":"Dataset Size Recovery from LoRA Weights","created":"2024-06-27","cited_by_count":7,"area":"safety"},{"id":"2111.01363","title":"Knowledge Cross-Distillation for Membership Privacy","created":"2021-11-02","cited_by_count":6,"area":"safety"},{"id":"2204.04950","title":"Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data","created":"2022-04-11","cited_by_count":6,"area":null},{"id":"2208.08114","title":"An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models","created":"2022-08-17","cited_by_count":6,"area":"safety"},{"id":"2205.06469","title":"l-Leaks: Membership Inference Attacks with Logits","created":"2022-05-13","cited_by_count":5,"area":"safety"},{"id":"2403.08487","title":"Model Will Tell: Training Membership Inference for Diffusion Models","created":"2024-03-13","cited_by_count":5,"area":"diffusion"},{"id":"2412.02467","title":"DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators","created":"2024-12-03","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":"2204.06297","title":"Enabling Synthetic Data adoption in regulated domains","created":"2022-04-13","cited_by_count":4,"area":null},{"id":"2509.05350","title":"Ensembling Membership Inference Attacks Against Tabular Generative Models","created":"2025-09-02","cited_by_count":4,"area":"safety"},{"id":"2509.13046","title":"MIA-EPT: Membership Inference Attack via Error Prediction for Tabular Data","created":"2025-09-16","cited_by_count":4,"area":"diffusion"},{"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":"2511.01952","title":"Black-Box Membership Inference Attack for LVLMs via Prior Knowledge-Calibrated Memory Probing","created":"2025-11-03","cited_by_count":4,"area":"mllm"},{"id":"2105.13144","title":"Causally Constrained Data Synthesis for Private Data Release","created":"2021-05-27","cited_by_count":3,"area":"synth"},{"id":"2109.03975","title":"Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning","created":"2021-09-08","cited_by_count":3,"area":"safety"},{"id":"2202.05469","title":"Privacy-preserving Generative Framework Against Membership Inference Attacks","created":"2022-02-11","cited_by_count":3,"area":"safety"},{"id":"2304.11336","title":"Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders","created":"2023-04-22","cited_by_count":3,"area":null},{"id":"2306.00687","title":"Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review","created":"2023-06-01","cited_by_count":3,"area":null},{"id":"2406.18812","title":"A Survey on Privacy Attacks Against Digital Twin Systems in AI-Robotics","created":"2024-06-27","cited_by_count":3,"area":null},{"id":"2410.02246","title":"PFGuard: A Generative Framework with Privacy and Fairness Safeguards","created":"2024-10-03","cited_by_count":3,"area":null},{"id":"2509.07879","title":"Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning","created":"2025-09-09","cited_by_count":3,"area":"safety"},{"id":"2509.25003","title":"Score-based Membership Inference on Diffusion Models","created":"2025-09-29","cited_by_count":3,"area":"diffusion"},{"id":"2511.20497","title":"Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic","created":"2025-11-25","cited_by_count":3,"area":"safety"},{"id":"2512.08875","title":"When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation","created":"2025-12-09","cited_by_count":3,"area":null},{"id":"2204.07877","title":"Assessing Differentially Private Variational Autoencoders under Membership Inference","created":"2022-04-16","cited_by_count":2,"area":"safety"},{"id":"2310.10124","title":"A Comprehensive Study of Privacy Risks in Curriculum Learning","created":"2023-10-16","cited_by_count":2,"area":null},{"id":"2311.03172","title":"Preserving Privacy in GANs Against Membership Inference Attack","created":"2023-11-06","cited_by_count":2,"area":"safety"},{"id":"2402.04912","title":"Towards Biologically Plausible and Private Gene Expression Data Generation","created":"2024-02-07","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":"2406.15253","title":"Fingerprint Membership and Identity Inference Against Generative Adversarial Networks","created":"2024-06-21","cited_by_count":2,"area":null},{"id":"2408.06825","title":"Membership Inference Attack Against Masked Image Modeling","created":"2024-08-13","cited_by_count":2,"area":"safety"},{"id":"2501.06399","title":"GenAI Confessions: Black-box Membership Inference for Generative Image Models","created":"2025-01-11","cited_by_count":2,"area":"safety"},{"id":"2602.07126","title":"Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting","created":"2026-02-06","cited_by_count":2,"area":"safety"},{"id":"2206.00071","title":"Generative Models with Information-Theoretic Protection Against Membership Inference Attacks","created":"2022-05-31","cited_by_count":1,"area":"safety"},{"id":"2307.03118","title":"Quantum Solutions to the Privacy vs. Utility Tradeoff","created":"2023-07-06","cited_by_count":1,"area":null},{"id":"2307.04208","title":"On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise","created":"2023-07-09","cited_by_count":1,"area":null},{"id":"2307.15067","title":"Set-Membership Inference Attacks using Data Watermarking","created":"2023-06-22","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":"2404.00696","title":"Privacy Re-identification Attacks on Tabular GANs","created":"2024-03-31","cited_by_count":1,"area":null},{"id":"2404.09722","title":"VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication","created":"2024-04-15","cited_by_count":1,"area":null},{"id":"2409.03612","title":"VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data","created":"2024-09-05","cited_by_count":1,"area":null},{"id":"2409.10986","title":"Control-flow Reconstruction Attacks on Business Process Models","created":"2024-09-17","cited_by_count":1,"area":null},{"id":"2504.00758","title":"TAMIS: Tailored Membership Inference Attacks on Synthetic Data","created":"2025-04-01","cited_by_count":1,"area":"safety"},{"id":"2504.10555","title":"Beyond the Generative Learning Trilemma: Generative Model Assessment in Data Scarcity Domains","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2505.20955","title":"Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective","created":"2025-05-27","cited_by_count":1,"area":"diffusion"},{"id":"2506.19360","title":"SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2509.09488","title":"Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts","created":"2025-09-11","cited_by_count":1,"area":"diffusion"},{"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":"2601.22663","title":"Unsupervised Synthetic Image Attribution: Alignment and Disentanglement","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2602.01645","title":"Membership Inference Attack Against Music Diffusion Models via Generative Manifold Perturbation","created":"2026-02-02","cited_by_count":1,"area":"diffusion"},{"id":"2603.01544","title":"RA-Det: Towards Universal Detection of AI-Generated Images via Robustness Asymmetry","created":"2026-03-02","cited_by_count":1,"area":null}],"next_cursor":"100"}