# Protecting User Privacy in Remote Conversational Systems: A Privacy-Preserving framework based on text sanitization

arXiv: https://arxiv.org/abs/2306.08223 · HTML: https://mlsanity.com/paper/2306.08223/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2023.

## Built on by (most cited first, 23 of 23)

- [Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems](https://mlsanity.com/paper/2401.05778/) (2024)
- [CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following](https://mlsanity.com/paper/2403.03129/) (2024)
- [Split-and-Denoise: Protect large language model inference with local differential privacy](https://mlsanity.com/paper/2310.09130/) (2023)
- [PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration](https://mlsanity.com/paper/2406.01394/) (2024)
- [Privacy Preserving Prompt Engineering: A Survey](https://mlsanity.com/paper/2404.06001/) (2024)
- [RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service](https://mlsanity.com/paper/2412.12775/) (2024)
- [The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts](https://mlsanity.com/paper/2406.14318/) (2024)
- [EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs](https://mlsanity.com/paper/2402.05868/) (2024)
- [Operationalizing Data Minimization for Privacy-Preserving LLM Prompting](https://mlsanity.com/paper/2510.03662/) (2025)
- [What Makes a Good Natural Language Prompt?](https://mlsanity.com/paper/2506.06950/) (2025)
- [Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks](https://mlsanity.com/paper/2504.17421/) (2025)
- [ConfusionPrompt: Practical Private Inference for Online Large Language Models](https://mlsanity.com/paper/2401.00870/) (2023)
- [PFID: Privacy First Inference Delegation Framework for LLMs](https://mlsanity.com/paper/2406.12238/) (2024)
- [A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation](https://mlsanity.com/paper/2502.15233/) (2025)
- [MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction](https://mlsanity.com/paper/2509.23459/) (2025)
- [Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation](https://mlsanity.com/paper/2606.04067/) (2026)
- [LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice](https://mlsanity.com/paper/2501.10915/) (2025)
- [Confidential Prompting: Privacy-preserving LLM Inference on Cloud](https://mlsanity.com/paper/2409.19134/) (2024)
- [AiBAT: Artificial Intelligence/Instructions for Build, Assembly, and Test](https://mlsanity.com/paper/2410.02955/) (2024)
- [Stereo-LiDAR Fusion by Semi-Global Matching With Discrete Disparity-Matching Cost and Semidensification](https://mlsanity.com/paper/2504.05148/) (2025)
- [Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition](https://mlsanity.com/paper/2604.23413/) (2026)
- [Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models](https://mlsanity.com/paper/2606.03399/) (2026)
- [LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication](https://mlsanity.com/paper/2609.23193/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are gaining increasing attention due to their exceptional performance across numerous tasks. As a result, the general public utilize them as an influential tool for boosting their productivity while natural language processing researchers endeavor to employ them in solving existing or new research problems. Unfortunately, individuals can only access such powerful AIs through APIs, which ultimately leads to the transmission of raw data to the models' providers and increases the possibility of privacy data leakage. Current privacy-preserving methods for cloud-deployed language models aim to protect privacy information in the pre-training dataset or during the model training phase. However, they do not meet the specific challenges presented by the remote access approach of new large-scale language models. This paper introduces a novel task, "User Privacy Protection for Dialogue Models," which aims to safeguard sensitive user information from any possible disclosure while conversing with chatbots. We also present an evaluation scheme for this task, which covers evaluation metrics for privacy protection, data availability, and resistance to simulation attacks. Moreover, we propose the first framework for this task, namely privacy protection through text sanitization. Before sending the input to remote large models, it filters out the sensitive information, using several rounds of text sanitization based on privacy types that users define. Upon receiving responses from the larger model, our framework automatically restores privacy to ensure that the conversation goes smoothly, without intervention from the privacy filter. Experiments based on real-world datasets demonstrate the efficacy of our privacy-preserving approach against eavesdropping from potential attackers.