{"total":169,"rows":[{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"id":"2111.09679","title":"Enhanced Membership Inference Attacks against Machine Learning Models","created":"2021-11-18","cited_by_count":248,"area":"safety"},{"id":"2101.04535","title":"Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning","created":"2021-01-11","cited_by_count":199,"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":"2305.03010","title":"Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence","created":"2023-05-04","cited_by_count":81,"area":null},{"id":"2110.08324","title":"Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture","created":"2021-10-15","cited_by_count":67,"area":"safety"},{"id":"2106.06916","title":"Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization","created":"2021-06-13","cited_by_count":52,"area":"safety"},{"id":"2107.01614","title":"Survey: Leakage and Privacy at Inference Time","created":"2021-07-04","cited_by_count":44,"area":null},{"id":"2107.01154","title":"Gradient-Leakage Resilient Federated Learning","created":"2021-07-02","cited_by_count":40,"area":null},{"id":"2211.05568","title":"Unbiased Supervised Contrastive Learning","created":"2022-11-10","cited_by_count":40,"area":null},{"id":"2207.05801","title":"RelaxLoss: Defending Membership Inference Attacks without Losing Utility","created":"2022-07-12","cited_by_count":37,"area":"safety"},{"id":"2102.04140","title":"Quantifying and Mitigating Privacy Risks of Contrastive Learning","created":"2021-02-08","cited_by_count":36,"area":"safety"},{"id":"2107.10171","title":"Leave-one-out Unfairness","created":"2021-07-21","cited_by_count":36,"area":null},{"id":"2109.05659","title":"Source Inference Attacks in Federated Learning","created":"2021-09-13","cited_by_count":34,"area":null},{"id":"2411.02902","title":"Membership Inference Attacks against Large Vision-Language Models","created":"2024-11-05","cited_by_count":33,"area":"mllm"},{"id":"2411.11315","title":"A Review on Machine Unlearning","created":"2024-11-18","cited_by_count":32,"area":"safety"},{"id":"2402.04013","title":"Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses","created":"2024-02-06","cited_by_count":30,"area":null},{"id":"2208.11180","title":"Auditing Membership Leakages of Multi-Exit Networks","created":"2022-08-23","cited_by_count":27,"area":null},{"id":"2306.14123","title":"Privacy and Fairness in Federated Learning: on the Perspective of Trade-off","created":"2023-06-25","cited_by_count":26,"area":null},{"id":"2107.06018","title":"This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces","created":"2021-07-13","cited_by_count":25,"area":null},{"id":"2111.04906","title":"The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection","created":"2021-11-09","cited_by_count":25,"area":null},{"id":"2202.03460","title":"Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning","created":"2022-02-07","cited_by_count":24,"area":"safety"},{"id":"2409.03868","title":"Few-shot Adaptation of Medical Vision-Language Models","created":"2024-09-05","cited_by_count":24,"area":"mllm"},{"id":"2108.12318","title":"CAPE: Context-Aware Private Embeddings for Private Language Learning","created":"2021-08-27","cited_by_count":22,"area":null},{"id":"2404.00473","title":"Privacy Backdoors: Stealing Data with Corrupted Pretrained Models","created":"2024-03-30","cited_by_count":22,"area":null},{"id":"2301.01197","title":"Backdoor Attacks Against Dataset Distillation","created":"2023-01-03","cited_by_count":21,"area":null},{"id":"2506.03179","title":"Vid-SME: Membership Inference Attacks against Large Video Understanding Models","created":"2025-05-29","cited_by_count":19,"area":"mllm"},{"id":"2104.15129","title":"Stealthy Backdoors as Compression Artifacts","created":"2021-04-30","cited_by_count":18,"area":null},{"id":"2105.12049","title":"Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs","created":"2021-05-25","cited_by_count":18,"area":null},{"id":"2112.13178","title":"Gradient Leakage Attack Resilient Deep Learning","created":"2021-12-25","cited_by_count":18,"area":null},{"id":"2407.13863","title":"A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attacks","created":"2024-07-18","cited_by_count":18,"area":null},{"id":"2104.04268","title":"Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication","created":"2021-04-09","cited_by_count":17,"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":"2303.17051","title":"Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation","created":"2023-03-29","cited_by_count":17,"area":null},{"id":"2106.12478","title":"Teacher Model Fingerprinting Attacks Against Transfer Learning","created":"2021-06-23","cited_by_count":16,"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":"2106.03785","title":"Generative Adversarial Networks: A Survey Towards Private and Secure Applications","created":"2021-06-07","cited_by_count":15,"area":null},{"id":"2206.07156","title":"Federated Multi-organ Segmentation with Inconsistent Labels","created":"2022-06-14","cited_by_count":15,"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":"2303.11078","title":"Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection","created":"2023-03-20","cited_by_count":14,"area":null},{"id":"2307.08424","title":"Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model","created":"2023-07-17","cited_by_count":14,"area":"diffusion"},{"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":"2402.01096","title":"Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance","created":"2024-02-02","cited_by_count":14,"area":null},{"id":"2103.05472","title":"Differentially Private Imaging via Latent Space Manipulation","created":"2021-03-08","cited_by_count":13,"area":null},{"id":"2103.15753","title":"Privacy and Trust Redefined in Federated Machine Learning","created":"2021-03-29","cited_by_count":13,"area":null},{"id":"2211.15893","title":"Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise","created":"2022-11-29","cited_by_count":13,"area":null},{"id":"2204.09391","title":"You Are What You Write: Preserving Privacy in the Era of Large Language Models","created":"2022-04-20","cited_by_count":12,"area":null},{"id":"2210.13111","title":"Federated Learning and Meta Learning: Approaches, Applications, and Directions","created":"2022-10-24","cited_by_count":12,"area":null},{"id":"2304.11285","title":"Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks","created":"2023-04-22","cited_by_count":12,"area":"safety"},{"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":"2310.00222","title":"Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning","created":"2023-09-30","cited_by_count":11,"area":"safety"},{"id":"2109.10870","title":"SoK: Machine Learning Governance","created":"2021-09-20","cited_by_count":10,"area":null},{"id":"2112.02230","title":"SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning","created":"2021-12-04","cited_by_count":10,"area":null},{"id":"2209.01215","title":"Exploiting Fairness to Enhance Sensitive Attributes Reconstruction","created":"2022-09-02","cited_by_count":10,"area":null},{"id":"2311.14056","title":"DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release","created":"2023-11-23","cited_by_count":10,"area":"safety"},{"id":"2302.09813","title":"Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare","created":"2023-02-20","cited_by_count":9,"area":null},{"id":"2504.02199","title":"ESC: Erasing Space Concept for Knowledge Deletion","created":"2025-04-03","cited_by_count":9,"area":null},{"id":"2112.12998","title":"DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning","created":"2021-12-24","cited_by_count":8,"area":null},{"id":"2302.10827","title":"AutoML in The Wild: Obstacles, Workarounds, and Expectations","created":"2023-02-21","cited_by_count":8,"area":null},{"id":"2212.04371","title":"Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy","created":"2022-12-08","cited_by_count":7,"area":null},{"id":"2308.03573","title":"When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection","created":"2023-08-07","cited_by_count":7,"area":null},{"id":"2308.15099","title":"Probabilistic Dataset Reconstruction from Interpretable Models","created":"2023-08-29","cited_by_count":7,"area":null},{"id":"2406.13433","title":"Certification for Differentially Private Prediction in Gradient-Based Training","created":"2024-06-19","cited_by_count":7,"area":null},{"id":"2407.01919","title":"A Method to Facilitate Membership Inference Attacks in Deep Learning Models","created":"2024-07-02","cited_by_count":7,"area":"safety"},{"id":"2408.13161","title":"Say No to Freeloader: Protecting Intellectual Property of Your Deep Model","created":"2024-08-23","cited_by_count":7,"area":null},{"id":"2409.18222","title":"Trustworthy AI: Securing Sensitive Data in Large Language Models","created":"2024-09-26","cited_by_count":7,"area":null},{"id":"2411.05743","title":"Free Record-Level Privacy Risk Evaluation Through Artifact-Based Methods","created":"2024-11-08","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":"2111.02374","title":"Can I use this publicly available dataset to build commercial AI software? -- A Case Study on Publicly Available Image Datasets","created":"2021-11-03","cited_by_count":6,"area":null},{"id":"2210.12651","title":"Unsupervised Non-transferable Text Classification","created":"2022-10-23","cited_by_count":6,"area":null},{"id":"2211.05629","title":"Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises","created":"2022-11-03","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":"2401.10744","title":"FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models","created":"2024-01-19","cited_by_count":6,"area":"lr"},{"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":"2408.11546","title":"Memorization in In-Context Learning","created":"2024-08-21","cited_by_count":6,"area":null},{"id":"2110.05524","title":"Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference","created":"2021-10-11","cited_by_count":5,"area":"safety"},{"id":"2310.13786","title":"Fundamental Limits of Membership Inference Attacks on Machine Learning Models","created":"2023-10-20","cited_by_count":5,"area":"safety"},{"id":"2410.05159","title":"MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense","created":"2024-10-07","cited_by_count":5,"area":null},{"id":"2506.07888","title":"SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark","created":"2025-06-09","cited_by_count":5,"area":null},{"id":"2105.06300","title":"Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey","created":"2021-05-13","cited_by_count":4,"area":null},{"id":"2203.09943","title":"Training a Tokenizer for Free with Private Federated Learning","created":"2022-03-15","cited_by_count":4,"area":null},{"id":"2204.06963","title":"Finding MNEMON: Reviving Memories of Node Embeddings","created":"2022-04-14","cited_by_count":4,"area":null},{"id":"2209.04022","title":"Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions","created":"2022-09-08","cited_by_count":4,"area":null},{"id":"2210.00891","title":"Information Removal at the bottleneck in Deep Neural Networks","created":"2022-09-30","cited_by_count":4,"area":null},{"id":"2212.11810","title":"GAN-based Domain Inference Attack","created":"2022-12-22","cited_by_count":4,"area":null},{"id":"2311.17453","title":"Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions","created":"2023-11-29","cited_by_count":4,"area":null},{"id":"2312.04692","title":"Diffence: Fencing Membership Privacy With Diffusion Models","created":"2023-12-07","cited_by_count":4,"area":"diffusion"},{"id":"2404.17674","title":"Center-Based Relaxed Learning Against Membership Inference Attacks","created":"2024-04-26","cited_by_count":4,"area":"safety"},{"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":"2502.07830","title":"Captured by Captions: On Memorization and its Mitigation in CLIP Models","created":"2025-02-11","cited_by_count":4,"area":null},{"id":"2503.22413","title":"Instance-Level Data-Use Auditing of Visual ML Models","created":"2025-03-28","cited_by_count":4,"area":"safety"},{"id":"2201.06093","title":"Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)","created":"2022-01-16","cited_by_count":3,"area":null},{"id":"2201.08786","title":"FedComm: Federated Learning as a Medium for Covert Communication","created":"2022-01-21","cited_by_count":3,"area":null},{"id":"2202.06091","title":"TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding","created":"2022-02-12","cited_by_count":3,"area":null},{"id":"2203.16463","title":"Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning","created":"2022-03-30","cited_by_count":3,"area":"safety"},{"id":"2304.14831","title":"Earning Extra Performance from Restrictive Feedbacks","created":"2023-04-28","cited_by_count":3,"area":null},{"id":"2307.06123","title":"SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark","created":"2023-07-12","cited_by_count":3,"area":"safety"},{"id":"2406.05660","title":"Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models","created":"2024-06-09","cited_by_count":3,"area":"safety"},{"id":"2409.08170","title":"AD-Lite Net: A Lightweight and Concatenated CNN Model for Alzheimer's Detection from MRI Images","created":"2024-09-12","cited_by_count":3,"area":null}],"next_cursor":"100"}