PrivAnalogy: An Analogy Mechanism-Based Privacy Protection Framework for LLM Prompts
Summary
User prompts sent to cloud-based LLMs can expose sensitive information, and small input changes can drastically alter LLM responses, making privacy protection difficult. PrivAnalogy is a framework that protects privacy by transforming sensitive data in prompts into analogous expressions on the user's device before sending them to the LLM, then converting responses back to reflect the user's original intent. Testing shows the framework resists prompt inversion attacks (where someone tries to extract the original sensitive data from the LLM's response) significantly better than comparable methods while keeping response quality high.
Solution / Mitigation
PrivAnalogy uses two core components: an analogy selection module that applies local differential privacy (a mathematical privacy protection technique) to convert sensitive content into analogous expressions, and an analogy reversion module that restores semantic alignment between the LLM's response and the original prompt to ensure accurate answers.
Classification
Original source: http://ieeexplore.ieee.org/document/11617318
First tracked: September 3, 2026 at 08:02 PM
Classified by LLM (prompt v3) · confidence: 92%