Intelligent Privacy Assistant Model for LLM Data Protection
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Solution Overview
Problem
Current Large Language Models (LLMs) face significant privacy risks due to unrestricted access to user data, leading to potential data leakage and privacy violations, especially when deployed on personal devices.
Innovation Solution
An intelligent privacy assistant (IPA) model is proposed, utilizing a three-level privacy protection framework. This includes a rule-based privacy checklist, a personalized learning module, and a reasoning module that leverages large language models for enhanced privacy judgment and reasoning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are deployed on personal devices with unrestricted access to user data, then personalized service and productivity are improved, but privacy risks and data leakage vulnerabilities increase
Solution Approach 1:
The patent introduces a privacy filtering module as an intermediary between the LLM and user data. This module acts as a mediator that intercepts, analyzes, and filters data before it reaches the LLM, preventing direct access to sensitive information while still enabling personalized service through processed and sanitized data inputs.
Solution Approach 2:
The system segments the data processing architecture into distinct components: a privacy filtering module that handles sensitive data separately from the main LLM processing pipeline. This segmentation allows the LLM to receive processed, desensitized data while the filtering module independently manages privacy protection, resolving the contradiction between access and protection.
2Object-affected harmful factors
If data cleaning approaches remove pre-defined privacy patterns, then privacy protection is improved, but context understanding ability deteriorates and errors increase
Solution Approach 1:
The patent changes the operational parameters of the filtering system from rigid pattern-matching to dynamic context-aware filtering. The system adjusts its filtering behavior based on contextual analysis, allowing it to distinguish between actual privacy violations and benign uses of similar patterns, thereby maintaining both privacy protection and context understanding accuracy.
Solution Approach 2:
The system implements feedback mechanisms where the LLM's contextual understanding informs the privacy filtering decisions. The filtering module continuously learns from contextual patterns and adjusts its protection strategies accordingly, preventing over-filtering of legitimate content while maintaining privacy safeguards.
3Extent of automation
If LLMs operate as black-box models, then model complexity and reasoning power are improved, but privacy explanation capability and user trust deteriorate
Solution Approach 1:
The patent introduces an explanation generation module as an intermediary that translates the black-box LLM's privacy-related decisions into human-understandable explanations. This mediator component bridges the gap between the complex automated reasoning of the LLM and the user's need for transparency, converting internal model states into interpretable privacy explanations.
Solution Approach 2:
The system creates simplified copies or representations of the LLM's privacy decision-making process that can be explained to users. Rather than exposing the complex original model, the system generates simplified models or traces that replicate the essential reasoning path, making privacy explanations comprehensible while preserving the full model's reasoning power.
Data Source
AI summary
A system includes a text receiver, a rule-based privacy checklist module, a personalized learning module, and a reasoning module. The text receiver converts user input into a machine-readable format. The rule-based privacy checklist module retrieves privacy norms from a database, processes these norms into annotated rules, and compares input data against them to create a structured report. The personalized learning module, using a fine-tuned BERT, classifies contextual information for privacy violations and generates a local privacy report. The reasoning module integrates multi-turn dialogues, applies chain-of-thought reasoning to evaluate the entire context for private information, and delivers comprehensive cloud-based privacy judgments and reports.