Sensitive Information Filtering in ML Output Generation
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Solution Overview
Problem
Machine learning models inadvertently leak sensitive information when processing ambiguous input content, leading to potential data breaches and violations of privacy regulations.
Innovation Solution
An information protection process that prioritizes acoustic information over context to determine if sensitive information is supported by the input, and if not, omits or deprioritizes its inclusion in the output, thereby preventing leakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning models use context information to generate output content, then the user experience is enhanced by predicting what information a user wants, but sensitive information may be inadvertently leaked
Solution Approach 1:
The system performs preliminary actions by detecting sensitive information in the input content before generating output content. This early detection allows the system to prevent sensitive information leakage before it can occur during the content generation process, while still maintaining the ability to provide helpful responses to users.
Solution Approach 2:
The system extracts and removes sensitive information from the input content before processing. By taking out the harmful element (sensitive information) separately, the system can generate useful output content without inadvertently leaking confidential data, thus resolving the contradiction between helpfulness and security.
2Measurement precision
If machine learning models access sensitive information during training, then the models can provide more accurate predictions, but the possibility of inadvertent disclosure increases
Solution Approach 1:
The system performs preliminary detection of sensitive information in input content before the machine learning model processes it. This preliminary action ensures that even though the model has been trained on sensitive information for accuracy, it cannot inadvertently disclose such information during operation, thus maintaining both prediction accuracy and information confidentiality.
Solution Approach 2:
The system introduces an intermediary layer (sensitive information detection mechanism) between the input content and the machine learning model. This intermediary detects and handles sensitive information separately, allowing the model to maintain its predictive capabilities while preventing unauthorized disclosure of confidential data.
3Reliability
If the system scrutinizes input content to detect sensitive information, then information security is improved, but processing time and complexity increase
Solution Approach 1:
The system applies partial action by detecting only specific sensitive information patterns in the input content rather than performing exhaustive analysis on all content. This selective detection approach maintains high information security while minimizing additional processing time and system complexity.
Data Source
AI summary
A method, computer program product, and computing system for receiving input content that may include sensitive information for processing by a machine learning model; processing the input content with the machine learning model to generate output content; processing the output content to determine if the output content includes any sensitive information; and if the output content includes any sensitive information, scrutinizing the input content to determine if the input content supports the inclusion of the sensitive information in the output content.