# CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications

arXiv: https://arxiv.org/abs/2410.13387 · HTML: https://mlsanity.com/paper/2410.13387/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2024.

## Built on by (most cited first, 30 of 33)

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- [Why Semantic Entropy Fails: Geometry-Aware and Calibrated Uncertainty for Policy Optimization](https://mlsanity.com/paper/2605.21801/) (2026)
- ["Before, I Asked My Mom, Now I Ask ChatGPT": Visual Privacy Management with Generative AI for Blind and Low-Vision People](https://mlsanity.com/paper/2507.00286/) (2025)
- [Beyond Data Privacy: New Privacy Risks for Large Language Models](https://mlsanity.com/paper/2509.14278/) (2025)
- [Helping Johnny Make Sense of Privacy Policies with LLMs](https://mlsanity.com/paper/2501.16033/) (2025)
- ["Lost-in-the-Later": Framework for Quantifying Contextual Grounding in Large Language Models](https://mlsanity.com/paper/2507.05424/) (2025)
- [How do we measure privacy in text? A survey of text anonymization metrics](https://mlsanity.com/paper/2512.01109/) (2025)
- [SAGE: Answer-Conditioned Uncertainty Targets for Verbal Uncertainty Alignment](https://mlsanity.com/paper/2606.11512/) (2026)
- [Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM Inference](https://mlsanity.com/paper/2509.12152/) (2025)
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- [Food4All: An Agentic Framework and Benchmark for Food Resource Navigation with Adaptive User Understanding](https://mlsanity.com/paper/2510.18289/) (2025)
- [A Longitudinal Measurement of Privacy Policy Evolution for Large Language Models](https://mlsanity.com/paper/2511.21758/) (2025)
- [Privacy in Human-AI Romantic Relationships: Concerns, Boundaries, and Agency](https://mlsanity.com/paper/2601.16824/) (2026)
- [From Human-Human Collaboration to Human-Agent Collaboration: A Vision, Design Philosophy, and an Empirical Framework for Achieving Successful Partnerships Between Humans and LLM Agents](https://mlsanity.com/paper/2602.05987/) (2026)
- [User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios](https://mlsanity.com/paper/2510.20721/) (2025)
- [Understanding Users' Privacy Reasoning and Behaviors During Chatbot Use to Support Meaningful Agency in Privacy](https://mlsanity.com/paper/2601.18125/) (2026)
- [PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies](https://mlsanity.com/paper/2603.09214/) (2026)
- [Confidence Laundering in Agent Systems: Why Uncertainty Needs a Latent Carrier](https://mlsanity.com/paper/2606.20662/) (2026)
- [DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization](https://mlsanity.com/paper/2601.04641/) (2026)
- [From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management](https://mlsanity.com/paper/2602.05016/) (2026)
- [Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agents](https://mlsanity.com/paper/2603.19416/) (2026)
- [The Privacy Guardian Agent: Towards Trustworthy AI Privacy Agents](https://mlsanity.com/paper/2604.21455/) (2026)
- [PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions](https://mlsanity.com/paper/2605.20206/) (2026)
- [SupraBench: A Benchmark for Supramolecular Chemistry](https://mlsanity.com/paper/2606.13477/) (2026)
- [Object-Centric Environment Modeling for Agentic Tasks](https://mlsanity.com/paper/2607.02846/) (2026)

## Abstract (arXiv)

The rise of end-user applications powered by large language models (LLMs), including both conversational interfaces and add-ons to existing graphical user interfaces (GUIs), introduces new privacy challenges. However, many users remain unaware of the risks. This paper explores methods to increase user awareness of privacy risks associated with LLMs in end-user applications. We conducted five co-design workshops to uncover user privacy concerns and their demand for contextual privacy information within LLMs. Based on these insights, we developed CLEAR (Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation), a just-in-time contextual assistant designed to help users identify sensitive information, summarize relevant privacy policies, and highlight potential risks when sharing information with LLMs. We evaluated the usability and usefulness of CLEAR across two example domains: ChatGPT and the Gemini plugin in Gmail. Our findings demonstrated that CLEAR is easy to use and improves users' understanding of data practices and privacy risks. We also discussed LLM's duality in posing and mitigating privacy risks, offering design and policy implications.