"Ghost of the past": identifying and resolving privacy leakage from LLM's memory through proactive user interaction

TL;DR

This paper introduces MemoAnalyzer, a system that identifies and visualizes private information in LLM memories, enabling proactive privacy management and reducing privacy risks during human-LLM interactions.

Contribution

It presents a novel prompt-based method for detecting sensitive data in LLM memories and a visualization interface for user privacy control, addressing low privacy awareness issues.

Findings

MemoAnalyzer improves user privacy awareness and protection.

It maintains interaction speed while enhancing privacy controls.

Users can easily modify sensitive information in LLM memories.

Abstract

Memories, encompassing past inputs in context window and retrieval-augmented generation (RAG), frequently surface during human-LLM interactions, yet users are often unaware of their presence and the associated privacy risks. To address this, we propose MemoAnalyzer, a system for identifying, visualizing, and managing private information within memories. A semi-structured interview (N=40) revealed that low privacy awareness was the primary challenge, while proactive privacy control emerged as the most common user need. MemoAnalyzer uses a prompt-based method to infer and identify sensitive information from aggregated past inputs, allowing users to easily modify sensitive content. Background color temperature and transparency are mapped to inference confidence and sensitivity, streamlining privacy adjustments. A 5-day evaluation (N=36) comparing MemoAnalyzer with the default GPT setting and a manual modification baseline showed MemoAnalyzer significantly improved privacy awareness and protection without compromising interaction speed. Our study contributes to privacy-conscious LLM design, offering insights into privacy protection for Human-AI interactions.