Identification Data Encryption for Privacy-Safe Third-Party AI Processing
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Solution Overview
Problem
Enterprises face challenges in securely delivering confidential data to third-party AI models while ensuring data privacy, risking information leakage and financial losses.
Innovation Solution
An identification information encryption system comprising a terminal, first server end, and second server end, utilizing machine learning models to replace identification information with encryption information, generating de-identification confidential information, and processing it securely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If confidential information is uploaded to a third-party AI model for processing, then AI processing capability is improved, but information security deteriorates due to risk of data leakage
Solution Approach 1:
The patent extracts identification information from confidential data before uploading to third-party AI models. The first processor identifies and separates identification information (such as personal identifiers, account numbers, etc.) from the confidential information, removing these sensitive elements prior to external processing, thereby preventing potential data leakage while maintaining the utility of the remaining data for AI analysis
Solution Approach 2:
The patent performs de-identification processing as a preliminary action before the confidential information is transmitted to third-party AI models. By preprocessing the data to remove or encrypt identification information in advance, the system ensures that sensitive data is protected from the outset, eliminating security risks associated with third-party access while enabling subsequent AI processing
2Reliability
If identification information is removed from confidential information, then information security is improved, but data utility deteriorates due to loss of identifying characteristics
Solution Approach 1:
The patent creates a de-identified copy of the confidential information by replacing identification information with encryption information or placeholder data. This copying approach preserves the structure and non-identifying characteristics of the original data, allowing the de-identified copy to maintain analytical utility while eliminating privacy risks associated with the original identification information
Solution Approach 2:
The patent transforms identification information into encryption information through parameter changes such as substitution, masking, or hashing. This transformation maintains the data structure and analytical properties needed for AI processing while changing the identifying parameters to non-identifiable forms, thereby preserving data utility without compromising privacy
Data Source
AI summary
An identification information encryption system includes a terminal, a first server end, and a second server end. The terminal is configured to send confidential information, which includes general information and identification information. The first server end is coupled to the terminal, and includes a first processor and a memory. The first processor is configured to read the confidential information, determine content and coordinates of the identification information based on a first machine learning model, and replace the identification information with encryption information, to generate de-identification confidential information. The memory is coupled to the first processor, and is configured to store the content and the coordinates of the identification information. The second server end is coupled to the first server end, and includes a second processor. The second processor is configured to read the de-identification confidential information, and process the de-identification confidential information based on a second machine learning model.