mlsanityData to 14 Oct 2026

LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance

arXiv 2505.17145 ↗First posted 22 May 2025cs.CR cs.AIcited by 6

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Abstract (arXiv)

Large language models (LLMs) are increasingly applied in fields such as finance, education, and governance due to their ability to generate human-like text and adapt to specialized tasks. However, their widespread adoption raises critical concerns about data privacy and security, including the risk of sensitive data exposure. In this paper, we propose a security framework to enforce policy compliance and mitigate risks in LLM interactions. Our approach introduces three key innovations: (i) LLM-based policy enforcement: a customizable mechanism that enhances domain-specific detection of sensitive data. (ii) Dynamic policy customization: real-time policy adaptation and enforcement during user-LLM interactions to ensure compliance with evolving security requirements. (iii) Sensitive data anonymization: a format-preserving encryption technique that protects sensitive information while maintaining contextual integrity. Experimental results demonstrate that our framework effectively mitigates security risks while preserving the functional accuracy of LLM-driven tasks.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 26 of its 58 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

26 of 58 references.

References · 26

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  2. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
  3. 2025Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learningcited by 262
  4. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 186
  5. 2024On Protecting the Data Privacy of Large Language Models (LLMs): A Surveycited by 96
  6. 2023DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Passcited by 67
  7. 2022THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryptioncited by 57
  8. 2023DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineercited by 57
  9. 2023Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Servicescited by 43
  10. 2022Privately Fine-Tuning Large Language Models with Differential Privacycited by 39
  11. 2023Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protectioncited by 38
  12. 2022Just Fine-tune Twice: Selective Differential Privacy for Large Language Modelscited by 32
14 more
  1. 2023Prompting Frameworks for Large Language Models: A Surveycited by 29
  2. 2024Privacy Preserving Prompt Engineering: A Surveycited by 25
  3. 2023LLMs Can Understand Encrypted Prompt: Towards Privacy-Computing Friendly Transformerscited by 22
  4. 2023Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryptioncited by 19
  5. 2024Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT Modelscited by 16
  6. 2024No Free Lunch Theorem for Privacy-Preserving LLM Inferencecited by 13
  7. 2024The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Promptscited by 13
  8. 2023East: Efficient and Accurate Secure Transformer Framework for Inferencecited by 9
  9. 2023Privacy Preserving Large Language Models: ChatGPT Case Study Based Vision and Frameworkcited by 8
  10. 2024Privacy-Preserving Language Model Inference with Instance Obfuscationcited by 7
  11. 2024ObfuscaTune: Obfuscated Offsite Fine-tuning and Inference of Proprietary LLMs on Private Datasetscited by 4
  12. 2023ConfusionPrompt: Practical Private Inference for Online Large Language Modelscited by 3
  13. 2025Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptographycited by 2
  14. 2024Transparency, Security, and Workplace Training & Awareness in the Age of Generative AIcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025A Survey on Model Extraction Attacks and Defenses for Large Language Modelscited by 19
  2. 2025Training-Free Policy Violation Detection via Activation-Space Whitening in LLMscited by 6
  3. 2026From Performance to Purpose: A Sociotechnical Taxonomy for Evaluating Large Language Model Utilitycited by 0
  4. 2026A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliancecited by 0
  5. 2026π-RAG: Oblivious Retrieval via Semantic Quantization and Transcendental Addressing for Large Language Modelscited by 0
  6. 2026A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemescited by 0