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

TL;DR

Adanonymizer is an interactive tool that helps users balance privacy and output quality in human-LLM interactions by visualizing and adjusting a trade-off curve, reducing effort and improving user satisfaction.

Contribution

It introduces a novel interactive anonymization plugin with a visual interface for balancing privacy and utility in LLM interactions, supported by user studies and evaluations.

Findings

Users often disclose sensitive info despite privacy concerns.

Adanonymizer reduces user effort and improves perceived model performance.

It outperforms ablation and differential privacy methods in user preference.

Abstract

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.