{"total":321,"rows":[{"id":"2304.01373","title":"Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling","created":"2023-04-03","cited_by_count":1786,"area":null},{"id":"2303.04226","title":"A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT","created":"2023-03-07","cited_by_count":224,"area":null},{"id":"2308.07707","title":"Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening","created":"2023-08-15","cited_by_count":181,"area":"safety"},{"id":"2304.11158","title":"Emergent and Predictable Memorization in Large Language Models","created":"2023-04-21","cited_by_count":148,"area":null},{"id":"2303.11570","title":"Boundary Unlearning","created":"2023-03-21","cited_by_count":134,"area":"safety"},{"id":"2301.05603","title":"A Comprehensive Survey of Dataset Distillation","created":"2023-01-13","cited_by_count":115,"area":null},{"id":"2311.06062","title":"Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration","created":"2023-11-10","cited_by_count":98,"area":"safety"},{"id":"2107.06641","title":"Trustworthy AI: A Computational Perspective","created":"2021-07-12","cited_by_count":93,"area":null},{"id":"2308.07061","title":"Machine Unlearning: Solutions and Challenges","created":"2023-08-14","cited_by_count":93,"area":"safety"},{"id":"2201.06640","title":"Towards Adversarial Evaluations for Inexact Machine Unlearning","created":"2022-01-17","cited_by_count":84,"area":"safety"},{"id":"2301.00265","title":"Source-Free Unsupervised Domain Adaptation: A Survey","created":"2022-12-31","cited_by_count":80,"area":null},{"id":"2310.14735","title":"Unleashing the potential of prompt engineering for large language models","created":"2023-10-23","cited_by_count":72,"area":null},{"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":"2205.03257","title":"Synthetic Data -- what, why and how?","created":"2022-05-06","cited_by_count":67,"area":null},{"id":"2302.12580","title":"Membership Inference Attacks against Synthetic Data through Overfitting Detection","created":"2023-02-24","cited_by_count":66,"area":"safety"},{"id":"2310.15007","title":"Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models","created":"2023-10-23","cited_by_count":56,"area":"safety"},{"id":"2305.06360","title":"Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy","created":"2023-05-10","cited_by_count":51,"area":"safety"},{"id":"2305.16157","title":"Training Data Extraction From Pre-trained Language Models: A Survey","created":"2023-05-25","cited_by_count":45,"area":null},{"id":"2306.09345","title":"Evaluating Data Attribution for Text-to-Image Models","created":"2023-06-15","cited_by_count":45,"area":null},{"id":"2409.19732","title":"Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement","created":"2024-09-29","cited_by_count":45,"area":"safety"},{"id":"2107.01614","title":"Survey: Leakage and Privacy at Inference Time","created":"2021-07-04","cited_by_count":44,"area":null},{"id":"2405.20446","title":"Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation","created":"2024-05-30","cited_by_count":44,"area":"rag"},{"id":"2409.01380","title":"Membership Inference Attacks Against In-Context Learning","created":"2024-09-02","cited_by_count":43,"area":"safety"},{"id":"2210.10750","title":"Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries","created":"2022-10-19","cited_by_count":40,"area":"safety"},{"id":"2310.00108","title":"Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study","created":"2023-09-29","cited_by_count":38,"area":"safety"},{"id":"2206.07975","title":"BlindFL: Vertical Federated Machine Learning without Peeking into Your Data","created":"2022-06-16","cited_by_count":36,"area":null},{"id":"2305.08005","title":"Beyond the Safeguards: Exploring the Security Risks of ChatGPT","created":"2023-05-13","cited_by_count":36,"area":null},{"id":"2106.11384","title":"Membership Inference on Word Embedding and Beyond","created":"2021-06-21","cited_by_count":35,"area":"safety"},{"id":"2109.05659","title":"Source Inference Attacks in Federated Learning","created":"2021-09-13","cited_by_count":34,"area":null},{"id":"2209.06997","title":"M^4I: Multi-modal Models Membership Inference","created":"2022-09-15","cited_by_count":34,"area":"safety"},{"id":"2309.08230","title":"A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services","created":"2023-09-15","cited_by_count":34,"area":"safety"},{"id":"2411.02902","title":"Membership Inference Attacks against Large Vision-Language Models","created":"2024-11-05","cited_by_count":33,"area":"mllm"},{"id":"2209.10117","title":"A Comprehensive Survey on Trustworthy Recommender Systems","created":"2022-09-21","cited_by_count":32,"area":null},{"id":"2210.10837","title":"On Learning Fairness and Accuracy on Multiple Subgroups","created":"2022-10-19","cited_by_count":32,"area":null},{"id":"2412.06157","title":"Membership Inference Attacks and Defenses in Federated Learning: A Survey","created":"2024-12-09","cited_by_count":31,"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":"2410.22235","title":"Auditing $f$-Differential Privacy in One Run","created":"2024-10-29","cited_by_count":30,"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":"2410.20142","title":"Mask-based Membership Inference Attacks for Retrieval-Augmented Generation","created":"2024-10-26","cited_by_count":29,"area":"rag"},{"id":"2305.13257","title":"Watermarking Text Data on Large Language Models for Dataset Copyright","created":"2023-05-22","cited_by_count":27,"area":"safety"},{"id":"2308.04604","title":"A Survey on Decentralized Federated Learning","created":"2023-08-08","cited_by_count":27,"area":null},{"id":"2502.05673","title":"The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions","created":"2025-02-08","cited_by_count":26,"area":null},{"id":"2308.16375","title":"A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications","created":"2023-08-31","cited_by_count":22,"area":null},{"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":"2310.07152","title":"No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML","created":"2023-10-11","cited_by_count":21,"area":null},{"id":"2403.08994","title":"Ethos: Rectifying Language Models in Orthogonal Parameter Space","created":"2024-03-13","cited_by_count":20,"area":null},{"id":"2210.08871","title":"Industry-Scale Orchestrated Federated Learning for Drug Discovery","created":"2022-10-17","cited_by_count":19,"area":null},{"id":"2407.10494","title":"Learning to Unlearn for Robust Machine Unlearning","created":"2024-07-15","cited_by_count":19,"area":"safety"},{"id":"2205.14055","title":"A Blessing of Dimensionality in Membership Inference through Regularization","created":"2022-05-27","cited_by_count":18,"area":"safety"},{"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":"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":"2503.19338","title":"Membership Inference Attacks on Large-Scale Models: A Survey","created":"2025-03-25","cited_by_count":18,"area":"mllm"},{"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":"2409.15398","title":"Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI","created":"2024-09-23","cited_by_count":16,"area":"safety"},{"id":"2202.12232","title":"Bounding Membership Inference","created":"2022-02-24","cited_by_count":15,"area":"safety"},{"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":"2208.10134","title":"Machine Learning with Confidential Computing: A Systematization of Knowledge","created":"2022-08-22","cited_by_count":15,"area":null},{"id":"2307.16680","title":"On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook","created":"2023-07-31","cited_by_count":15,"area":"diffusion"},{"id":"2406.06186","title":"A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks","created":"2024-06-10","cited_by_count":15,"area":"safety"},{"id":"2503.05657","title":"NoT: Federated Unlearning via Weight Negation","created":"2025-03-07","cited_by_count":15,"area":"safety"},{"id":"2202.01243","title":"Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference","created":"2022-02-02","cited_by_count":14,"area":"safety"},{"id":"2306.00419","title":"Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond","created":"2023-06-01","cited_by_count":14,"area":null},{"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":"2402.00179","title":"De-identification is not enough: a comparison between de-identified and synthetic clinical notes","created":"2024-01-31","cited_by_count":14,"area":"safety"},{"id":"2410.04490","title":"A Large-Scale Exploit Instrumentation Study of AI/ML Supply Chain Attacks in Hugging Face Models","created":"2024-10-06","cited_by_count":14,"area":null},{"id":"2312.02052","title":"DUCK: Distance-based Unlearning via Centroid Kinematics","created":"2023-12-04","cited_by_count":13,"area":"safety"},{"id":"2402.09225","title":"Is My Data in Your AI? Membership Inference Test (MINT) applied to Face Biometrics","created":"2024-02-14","cited_by_count":13,"area":"safety"},{"id":"2404.03862","title":"Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data","created":"2024-04-05","cited_by_count":13,"area":"alignment"},{"id":"2501.14224","title":"Top Ten Challenges Towards Agentic Neural Graph Databases","created":"2025-01-24","cited_by_count":13,"area":"agents"},{"id":"2504.14657","title":"A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs","created":"2025-04-20","cited_by_count":13,"area":null},{"id":"2506.11680","title":"Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information","created":"2025-06-13","cited_by_count":13,"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":"2311.11415","title":"A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models","created":"2023-11-19","cited_by_count":12,"area":null},{"id":"2503.13572","title":"VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contamination","created":"2025-03-17","cited_by_count":12,"area":null},{"id":"2104.08273","title":"Membership Inference Attacks on Knowledge Graphs","created":"2021-04-16","cited_by_count":11,"area":"safety"},{"id":"2308.09881","title":"Generative Adversarial Networks Unlearning","created":"2023-08-19","cited_by_count":11,"area":"safety"},{"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":"2404.14296","title":"Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach","created":"2024-04-22","cited_by_count":11,"area":"safety"},{"id":"2407.21248","title":"Adaptive Pre-training Data Detection for Large Language Models via Surprising Tokens","created":"2024-07-30","cited_by_count":11,"area":null},{"id":"2506.13972","title":"Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble","created":"2025-06-16","cited_by_count":11,"area":"safety"},{"id":"2209.01215","title":"Exploiting Fairness to Enhance Sensitive Attributes Reconstruction","created":"2022-09-02","cited_by_count":10,"area":null},{"id":"2209.03175","title":"On the utility and protection of optimization with differential privacy and classic regularization techniques","created":"2022-09-07","cited_by_count":10,"area":null},{"id":"2302.03654","title":"A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection","created":"2023-02-07","cited_by_count":10,"area":null},{"id":"2503.07384","title":"Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs","created":"2025-03-10","cited_by_count":10,"area":"safety"},{"id":"2209.09239","title":"Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey","created":"2022-09-17","cited_by_count":9,"area":null},{"id":"2303.09767","title":"It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness","created":"2023-03-17","cited_by_count":9,"area":null},{"id":"2403.15230","title":"An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets","created":"2024-03-22","cited_by_count":9,"area":null},{"id":"2404.02462","title":"A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability","created":"2024-04-03","cited_by_count":9,"area":"safety"},{"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":"2505.19139","title":"The Eye of Sherlock Holmes: Uncovering User Private Attribute Profiling via Vision-Language Model Agentic Framework","created":"2025-05-25","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":"2211.02139","title":"Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity","created":"2022-11-03","cited_by_count":8,"area":null},{"id":"2302.07735","title":"Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge","created":"2023-02-13","cited_by_count":8,"area":"safety"},{"id":"2402.09299","title":"Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code","created":"2024-02-14","cited_by_count":8,"area":null},{"id":"2403.14772","title":"Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures","created":"2024-03-21","cited_by_count":8,"area":null},{"id":"2407.20181","title":"Blockchain for Large Language Model Security and Safety: A Holistic Survey","created":"2024-07-26","cited_by_count":8,"area":"safety"},{"id":"2604.00419","title":"G-Drift MIA: Membership Inference via Gradient-Induced Feature Drift in LLMs","created":"2026-04-01","cited_by_count":8,"area":"safety"},{"id":"2304.11436","title":"Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack","created":"2023-04-22","cited_by_count":7,"area":null},{"id":"2310.07219","title":"Improved Membership Inference Attacks Against Language Classification Models","created":"2023-10-11","cited_by_count":7,"area":"safety"},{"id":"2402.06674","title":"Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning","created":"2024-02-07","cited_by_count":7,"area":"safety"}],"next_cursor":"100"}