# Adanonymizer: Interactively Navigating and Balancing the Duality of Privacy and Output Performance in Human-LLM Interaction

arXiv: https://arxiv.org/abs/2410.15044 · HTML: https://mlsanity.com/paper/2410.15044/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2024.

## Built on by (most cited first, 6 of 6)

- [Characterizing Unintended Consequences in Human-GUI Agent Collaboration for Web Browsing](https://mlsanity.com/paper/2505.09875/) (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)
- [Model-Editing-Based Jailbreak against Safety-aligned Large Language Models](https://mlsanity.com/paper/2412.08201/) (2024)
- [Through Their Eyes: User Perceptions on Sensitive Attribute Inference of Social Media Videos by Visual Language Models](https://mlsanity.com/paper/2508.07658/) (2025)
- [Understanding Users' Privacy Reasoning and Behaviors During Chatbot Use to Support Meaningful Agency in Privacy](https://mlsanity.com/paper/2601.18125/) (2026)
- [Focused on the User, Overlooking the Risks: Security and Privacy Understandings, Practices and Challenges of Independent Chinese AI Agent Developers](https://mlsanity.com/paper/2606.03190/) (2026)

## Abstract (arXiv)

Current Large Language Models (LLMs) cannot support users to precisely balance privacy protection and output performance during individual consultations. We introduce Adanonymizer, an anonymization plug-in that allows users to control this balance by navigating a trade-off curve. A survey (N=221) revealed a privacy paradox, where users frequently disclosed sensitive information despite acknowledging privacy risks. The study further demonstrated that privacy risks were not significantly correlated with model output performance, highlighting the potential to navigate this trade-off. Adanonymizer normalizes privacy and utility ratings by type and automates the pseudonymization of sensitive terms based on user preferences, significantly reducing user effort. Its 2D color palette interface visualizes the privacy-utility trade-off, allowing users to adjust the balance by manipulating a point. An evaluation (N=36) compared Adanonymizer with ablation methods and differential privacy techniques, where Adanonymizer significantly reduced modification time, achieved better perceived model performance and overall user preference.