{"total":35,"rows":[{"id":"2305.17359","title":"DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text","created":"2023-05-27","cited_by_count":129,"area":null},{"id":"2303.15715","title":"Foundation Models and Fair Use","created":"2023-03-28","cited_by_count":116,"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":"2310.01424","title":"Identifying and Mitigating Privacy Risks Stemming from Language Models: A Survey","created":"2023-09-27","cited_by_count":30,"area":null},{"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":"2410.02650","title":"Undesirable Memorization in Large Language Models: A Survey","created":"2024-10-03","cited_by_count":29,"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":"2407.02943","title":"PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding","created":"2024-07-03","cited_by_count":24,"area":null},{"id":"2508.13152","title":"RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns","created":"2025-08-18","cited_by_count":24,"area":null},{"id":"2406.05392","title":"Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey","created":"2024-06-08","cited_by_count":23,"area":null},{"id":"2410.08190","title":"Poison-splat: Computation Cost Attack on 3D Gaussian Splatting","created":"2024-10-10","cited_by_count":18,"area":null},{"id":"2310.02469","title":"PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners","created":"2023-10-03","cited_by_count":14,"area":null},{"id":"2309.17319","title":"Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models","created":"2023-09-29","cited_by_count":12,"area":null},{"id":"2505.10212","title":"Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M","created":"2025-05-15","cited_by_count":12,"area":null},{"id":"2407.02596","title":"Towards More Realistic Extraction Attacks: An Adversarial Perspective","created":"2024-07-02","cited_by_count":8,"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":"2507.03367","title":"Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices","created":"2025-07-04","cited_by_count":6,"area":null},{"id":"2409.12020","title":"Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization","created":"2024-09-18","cited_by_count":4,"area":null},{"id":"2605.23158","title":"What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference","created":"2026-05-22","cited_by_count":4,"area":null},{"id":"2404.17143","title":"Quantifying Memorization and Detecting Training Data of Pre-trained Language Models using Japanese Newspaper","created":"2024-04-26","cited_by_count":2,"area":null},{"id":"2412.20087","title":"On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2510.07452","title":"PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing","created":"2025-10-08","cited_by_count":2,"area":null},{"id":"2602.18733","title":"A Prior-Aware Metric for Efficiently Distinguishing Memorization from Generalization in Large Language Models","created":"2026-02-21","cited_by_count":2,"area":null},{"id":"2411.07070","title":"On Active Privacy Auditing in Supervised Fine-tuning for White-Box Language Models","created":"2024-11-11","cited_by_count":1,"area":null},{"id":"2505.11557","title":"AC-LoRA: (Almost) Training-Free Access Control-Aware Multi-Modal LLMs","created":"2025-05-15","cited_by_count":1,"area":null},{"id":"2506.06060","title":"Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models","created":"2025-06-06","cited_by_count":1,"area":null},{"id":"2508.02573","title":"Guess or Recall? Training CNNs to Classify and Localize Memorization in LLMs","created":"2025-08-04","cited_by_count":1,"area":null},{"id":"2510.20612","title":"Black Box Absorption: LLMs Undermining Innovative Ideas","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2602.20461","title":"Nonparametric Teaching of Attention Learners","created":"2026-02-24","cited_by_count":1,"area":null},{"id":"2606.17110","title":"Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs","created":"2026-06-15","cited_by_count":1,"area":null},{"id":"2609.30935","title":"Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models","created":"2026-09-25","cited_by_count":1,"area":null},{"id":"2403.18205","title":"Exploring the Privacy Protection Capabilities of Chinese Large Language Models","created":"2024-03-27","cited_by_count":0,"area":null},{"id":"2510.10902","title":"Auditing Information Disclosure During Large-Scale Gradient-Based Training via Gradient Uniqueness","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2605.05224","title":"Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms","created":"2026-04-18","cited_by_count":0,"area":null},{"id":"2607.22489","title":"\\k{appa}-LoRA: Condition Numbers Reveal Which LoRA Matrices Worth Updating","created":"2026-07-24","cited_by_count":0,"area":null}],"next_cursor":null}