{"total":99,"rows":[{"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":"2203.05363","title":"Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)","created":"2022-03-10","cited_by_count":48,"area":null},{"id":"2408.12787","title":"LLM-PBE: Assessing Data Privacy in Large Language Models","created":"2024-08-23","cited_by_count":41,"area":null},{"id":"2310.18362","title":"SoK: Memorization in General-Purpose Large Language Models","created":"2023-10-24","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":"2208.12348","title":"SNAP: Efficient Extraction of Private Properties with Poisoning","created":"2022-08-25","cited_by_count":31,"area":null},{"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":"2401.00676","title":"Digger: Detecting Copyright Content Mis-usage in Large Language Model Training","created":"2024-01-01","cited_by_count":25,"area":null},{"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":"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":"2405.14106","title":"Nearly Tight Black-Box Auditing of Differentially Private Machine Learning","created":"2024-05-23","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":"2305.18447","title":"Unleashing the Power of Randomization in Auditing Differentially Private ML","created":"2023-05-29","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":"2309.17310","title":"Leave-one-out Distinguishability in Machine Learning","created":"2023-09-29","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":"2407.15100","title":"A General Framework for Data-Use Auditing of ML Models","created":"2024-07-21","cited_by_count":16,"area":null},{"id":"2212.07591","title":"Dissecting Distribution Inference","created":"2022-12-15","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":"2205.04525","title":"Towards a multi-stakeholder value-based assessment framework for algorithmic systems","created":"2022-05-09","cited_by_count":12,"area":null},{"id":"2302.01190","title":"On the Efficacy of Differentially Private Few-shot Image Classification","created":"2023-02-02","cited_by_count":12,"area":null},{"id":"2306.03962","title":"PILLAR: How to make semi-private learning more effective","created":"2023-06-06","cited_by_count":10,"area":null},{"id":"2306.03013","title":"Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning","created":"2023-06-05","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":"2404.09536","title":"Noiseless Privacy-Preserving Decentralized Learning","created":"2024-04-15","cited_by_count":9,"area":null},{"id":"2506.06108","title":"Synthetic Tabular Data: Methods, Attacks and Defenses","created":"2025-06-06","cited_by_count":9,"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":"2312.07861","title":"GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks","created":"2023-12-13","cited_by_count":8,"area":null},{"id":"2407.07557","title":"Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging","created":"2024-07-10","cited_by_count":8,"area":null},{"id":"2402.00906","title":"BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks","created":"2024-02-01","cited_by_count":7,"area":null},{"id":"2209.06529","title":"Data Privacy and Trustworthy Machine Learning","created":"2022-09-14","cited_by_count":6,"area":null},{"id":"2312.04542","title":"SoK: Unintended Interactions among Machine Learning Defenses and Risks","created":"2023-12-07","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":"2409.03274","title":"Recent Advances in Attack and Defense Approaches of Large Language Models","created":"2024-09-05","cited_by_count":6,"area":null},{"id":"2411.10614","title":"To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling","created":"2024-11-15","cited_by_count":6,"area":null},{"id":"2506.19399","title":"Automated Detection of Pre-training Text in Black-box LLMs","created":"2025-06-24","cited_by_count":6,"area":null},{"id":"2404.16847","title":"State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey","created":"2024-02-25","cited_by_count":5,"area":null},{"id":"2504.07170","title":"Trustworthy AI Must Account for Interactions","created":"2025-04-09","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":"2305.14936","title":"Trade-Offs Between Fairness and Privacy in Language Modeling","created":"2023-05-24","cited_by_count":4,"area":null},{"id":"2310.10659","title":"Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System","created":"2023-09-12","cited_by_count":4,"area":null},{"id":"2403.04149","title":"MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection","created":"2024-03-07","cited_by_count":4,"area":null},{"id":"2406.02744","title":"DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning","created":"2024-06-04","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":"2206.12342","title":"FEATHERS: Federated Architecture and Hyperparameter Search","created":"2022-06-24","cited_by_count":3,"area":null},{"id":"2307.13548","title":"Node Injection Link Stealing Attack","created":"2023-07-25","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":"2410.16975","title":"Publishing Neural Networks in Drug Discovery Might Compromise Training Data Privacy","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2411.11266","title":"VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs","created":"2024-11-18","cited_by_count":3,"area":null},{"id":"2507.16872","title":"CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage","created":"2025-07-22","cited_by_count":3,"area":null},{"id":"2510.19934","title":"Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via $f$-Differential Privacy","created":"2025-10-22","cited_by_count":3,"area":null},{"id":"2212.01233","title":"Safe machine learning model release from Trusted Research Environments: The SACRO-ML package","created":"2022-12-02","cited_by_count":2,"area":null},{"id":"2302.00766","title":"Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds","created":"2023-02-01","cited_by_count":2,"area":null},{"id":"2409.07415","title":"SoK: Security and Privacy Risks of Healthcare AI","created":"2024-09-11","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":"2211.04686","title":"Directional Privacy for Deep Learning","created":"2022-11-09","cited_by_count":1,"area":null},{"id":"2305.15697","title":"Privacy Protectability: An Information-theoretical Approach","created":"2023-05-25","cited_by_count":1,"area":null},{"id":"2308.02362","title":"Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings","created":"2023-07-26","cited_by_count":1,"area":null},{"id":"2310.12112","title":"A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses","created":"2023-10-18","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":"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":"2412.00696","title":"Intermediate Outputs Are More Sensitive Than You Think","created":"2024-12-01","cited_by_count":1,"area":null},{"id":"2501.16671","title":"Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI","created":"2025-01-28","cited_by_count":1,"area":null},{"id":"2503.13872","title":"Empirical Calibration and Metric Differential Privacy in Language Models","created":"2025-03-18","cited_by_count":1,"area":null},{"id":"2505.17092","title":"Covert Attacks on Machine Learning Training in Passively Secure MPC","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2505.20095","title":"Spurious Privacy Leakage in Neural Networks","created":"2025-05-26","cited_by_count":1,"area":null},{"id":"2505.23792","title":"Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things","created":"2025-05-26","cited_by_count":1,"area":null},{"id":"2507.04528","title":"Towards integration of Privacy Enhancing Technologies in Explainable Artificial Intelligence","created":"2025-07-06","cited_by_count":1,"area":null},{"id":"2510.08813","title":"The Model's Language Matters: A Comparative Privacy Analysis of LLMs","created":"2025-10-09","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":"2511.12668","title":"AI Bill of Materials and Beyond: Systematizing Security Assurance through the AI Risk Scanning (AIRS) Framework","created":"2025-11-16","cited_by_count":1,"area":null},{"id":"2601.12922","title":"Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy","created":"2026-01-19","cited_by_count":1,"area":null},{"id":"2210.13028","title":"Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens","created":"2022-10-24","cited_by_count":0,"area":null},{"id":"2303.12589","title":"Do Backdoors Assist Membership Inference Attacks?","created":"2023-03-22","cited_by_count":0,"area":null},{"id":"2304.12185","title":"DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase","created":"2023-04-20","cited_by_count":0,"area":null},{"id":"2307.10997","title":"DREAM: Domain-free Reverse Engineering Attributes of Black-box Model","created":"2023-07-20","cited_by_count":0,"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":"2406.01708","title":"SnatchML: Hijacking ML models without Training Access","created":"2024-06-03","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":"2501.02407","title":"Towards the Anonymization of the Language Modeling","created":"2025-01-05","cited_by_count":0,"area":null},{"id":"2502.00847","title":"SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models","created":"2025-02-02","cited_by_count":0,"area":null},{"id":"2506.16460","title":"Black-Box Privacy Attacks on Shared Representations in Multitask Learning","created":"2025-06-19","cited_by_count":0,"area":null},{"id":"2508.15141","title":"Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2510.09114","title":"On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning","created":"2025-10-10","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":"2604.13474","title":"Secure and Privacy-Preserving Vertical Federated Learning","created":"2026-04-15","cited_by_count":0,"area":null},{"id":"2605.21543","title":"Provable Joint Decontamination for Benchmarking Multiple Large Language Models","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2605.21938","title":"Optimal Guarantees for Auditing R\\'enyi Differentially Private Machine Learning","created":"2026-05-21","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},{"id":"2607.13541","title":"When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2608.28198","title":"Performative Privacy: When Differential Privacy Maximizes Utility","created":"2026-08-28","cited_by_count":0,"area":null}],"next_cursor":null}