{"total":37,"rows":[{"id":"2312.02003","title":"A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly","created":"2023-12-04","cited_by_count":387,"area":null},{"id":"2307.02046","title":"Recommender Systems in the Era of Large Language Models (LLMs)","created":"2023-07-05","cited_by_count":189,"area":null},{"id":"2306.05817","title":"How Can Recommender Systems Benefit from Large Language Models: A Survey","created":"2023-06-09","cited_by_count":142,"area":null},{"id":"2409.00088","title":"On-Device Language Models: A Comprehensive Review","created":"2024-08-26","cited_by_count":104,"area":null},{"id":"2308.12833","title":"Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities","created":"2023-08-24","cited_by_count":82,"area":null},{"id":"2310.19596","title":"LLMaAA: Making Large Language Models as Active Annotators","created":"2023-10-30","cited_by_count":79,"area":null},{"id":"2401.05778","title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","created":"2024-01-11","cited_by_count":66,"area":null},{"id":"2310.10383","title":"Privacy in Large Language Models: Attacks, Defenses and Future Directions","created":"2023-10-16","cited_by_count":51,"area":null},{"id":"2312.17493","title":"Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning","created":"2023-12-29","cited_by_count":41,"area":null},{"id":"2311.04044","title":"PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models","created":"2023-11-07","cited_by_count":37,"area":null},{"id":"2407.18369","title":"AI Safety in Generative AI Large Language Models: A Survey","created":"2024-07-06","cited_by_count":33,"area":null},{"id":"2310.09130","title":"Split-and-Denoise: Protect large language model inference with local differential privacy","created":"2023-10-13","cited_by_count":32,"area":null},{"id":"2305.13246","title":"Interactive Natural Language Processing","created":"2023-05-22","cited_by_count":31,"area":null},{"id":"2307.08925","title":"Integration of Large Language Models and Federated Learning","created":"2023-07-18","cited_by_count":31,"area":null},{"id":"2406.02958","title":"PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs","created":"2024-06-05","cited_by_count":29,"area":null},{"id":"2406.01394","title":"PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration","created":"2024-06-03","cited_by_count":28,"area":null},{"id":"2311.17431","title":"Grounding Foundation Models through Federated Transfer Learning: A General Framework","created":"2023-11-29","cited_by_count":24,"area":null},{"id":"2406.14322","title":"Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning","created":"2024-06-20","cited_by_count":24,"area":null},{"id":"2407.02956","title":"IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization","created":"2024-07-03","cited_by_count":23,"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":"2405.20681","title":"No Free Lunch Theorem for Privacy-Preserving LLM Inference","created":"2024-05-31","cited_by_count":13,"area":null},{"id":"2312.15603","title":"A Split-and-Privatize Framework for Large Language Model Fine-Tuning","created":"2023-12-25","cited_by_count":11,"area":null},{"id":"2404.05783","title":"A Survey on Responsible Generative AI: What to Generate and What Not","created":"2024-04-08","cited_by_count":8,"area":null},{"id":"2408.04643","title":"Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey","created":"2024-08-01","cited_by_count":8,"area":null},{"id":"2402.07342","title":"Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation","created":"2024-02-12","cited_by_count":7,"area":null},{"id":"2403.04124","title":"Privacy-preserving Fine-tuning of Large Language Models through Flatness","created":"2024-03-07","cited_by_count":6,"area":null},{"id":"2505.17145","title":"LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance","created":"2025-05-22","cited_by_count":6,"area":null},{"id":"2405.04032","title":"Locally Differentially Private In-Context Learning","created":"2024-05-07","cited_by_count":5,"area":null},{"id":"2410.16672","title":"The Tug of War Within: Mitigating the Fairness-Privacy Conflicts in Large Language Models","created":"2024-10-22","cited_by_count":5,"area":null},{"id":"2504.17421","title":"Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks","created":"2025-04-24","cited_by_count":4,"area":null},{"id":"2504.21036","title":"Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks?","created":"2025-04-28","cited_by_count":4,"area":null},{"id":"2406.12238","title":"PFID: Privacy First Inference Delegation Framework for LLMs","created":"2024-06-18","cited_by_count":3,"area":null},{"id":"2409.17642","title":"AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure","created":"2024-09-26","cited_by_count":2,"area":null},{"id":"2502.00305","title":"DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning","created":"2025-02-01","cited_by_count":2,"area":null},{"id":"2506.09452","title":"Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform","created":"2025-06-11","cited_by_count":2,"area":null},{"id":"2508.01638","title":"Semantic Encryption: Secure and Effective Interaction with Cloud-based Large Language Models via Semantic Transformation","created":"2025-08-03","cited_by_count":0,"area":null},{"id":"2510.05468","title":"AMAQ: Adaptive Mixed-bit Activation Quantization for Collaborative Parameter Efficient Fine-tuning","created":"2025-10-07","cited_by_count":0,"area":null}],"next_cursor":null}