# Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents

arXiv: https://arxiv.org/abs/2410.11906 · HTML: https://mlsanity.com/paper/2410.11906/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Question Answering for Privacy Policies: Combining Computational and Legal Perspectives](https://mlsanity.com/paper/1911.00841/) (2019)
- [Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning](https://mlsanity.com/paper/1802.02561/) (2018)
- [PolicyQA: A Reading Comprehension Dataset for Privacy Policies](https://mlsanity.com/paper/2010.02557/) (2020)

## Built on by (most cited first, 9 of 9)

- [A Survey on Trustworthy LLM Agents: Threats and Countermeasures](https://mlsanity.com/paper/2503.09648/) (2025)
- ["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)
- [Helping Johnny Make Sense of Privacy Policies with LLMs](https://mlsanity.com/paper/2501.16033/) (2025)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China](https://mlsanity.com/paper/2510.08986/) (2025)
- [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)
- [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)
- [A Systematic Evaluation of Traditional Privacy Policy Analysis Tools Against LLMs](https://mlsanity.com/paper/2607.17075/) (2026)

## Abstract (arXiv)

This paper presents a novel application of large language models (LLMs) to enhance user comprehension of privacy policies through an interactive dialogue agent. We demonstrate that LLMs significantly outperform traditional models in tasks like Data Practice Identification, Choice Identification, Policy Summarization, and Privacy Question Answering, setting new benchmarks in privacy policy analysis. Building on these findings, we introduce an innovative LLM-based agent that functions as an expert system for processing website privacy policies, guiding users through complex legal language without requiring them to pose specific questions. A user study with 100 participants showed that users assisted by the agent had higher comprehension levels (mean score of 2.6 out of 3 vs. 1.8 in the control group), reduced cognitive load (task difficulty ratings of 3.2 out of 10 vs. 7.8), increased confidence in managing privacy, and completed tasks in less time (5.5 minutes vs. 15.8 minutes). This work highlights the potential of LLM-based agents to transform user interaction with privacy policies, leading to more informed consent and empowering users in the digital services landscape.