Sensitive Data Control in Speech Recognition Systems
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Solution Overview
Problem
Current speech recognition systems lack the ability to effectively manage sensitive data privacy, often broadcasting personal or confidential information without user authentication, which can be inappropriate in various contexts such as medical prescriptions or financial information.
Innovation Solution
The system implements sensitive data controls by determining when user requests involve sensitive data and applying appropriate privacy controls, such as requesting passwords or enabling password protection, to ensure that such information is only disclosed with proper authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech recognition systems broadcast personal or confidential information without authentication, then ease of operation is improved, but user privacy protection deteriorates
Solution Approach 1:
The system performs preliminary classification of speech content to identify sensitive data categories (medical, financial, personal information) before broadcasting. Privacy controls and authentication requirements are established in advance based on the classified content type, ensuring privacy protection is built into the system workflow rather than added as an afterthought.
Solution Approach 2:
Different privacy control mechanisms are applied to different types of sensitive information based on their classification. Medical information triggers one set of privacy controls, financial information triggers another, and personal information triggers a third. This localized approach ensures appropriate privacy protection for each data type while maintaining ease of operation for non-sensitive content.
2Object-affected harmful factors
If the system implements authentication requirements for sensitive data, then user privacy protection is improved, but device complexity increases
Solution Approach 1:
The speech processing system is segmented into distinct functional modules: speech recognition module, natural language understanding module, sensitive data classification module, and privacy control module. Each module handles a specific aspect of the process, making the overall complex system manageable through clear separation of concerns and specialized functionality in each segment.
Solution Approach 2:
A privacy control component acts as an intermediary between the speech processing system and the output broadcast. This intermediary receives classified sensitive data, determines appropriate privacy controls and authentication requirements, and manages the disclosure process. The intermediary simplifies complexity by centralizing privacy management logic in a dedicated component rather than distributing it throughout the entire system.
3Object-affected harmful factors
If the system classifies and controls sensitive data output, then user privacy protection is improved, but productivity decreases
Solution Approach 1:
The system changes parameters such as classification thresholds, privacy control levels, and authentication requirements based on the type of sensitive data detected. For example, medical information may require higher authentication levels than personal information. These parameter adjustments are made dynamically based on content analysis, allowing the system to maintain productivity by applying appropriate levels of control rather than uniform maximum security to all outputs.
Data Source
AI summary
A system is provided for determining privacy controls for output including sensitive data. A user may subscribe to receive an output in the future based on the occurrence of an event. The system may determine when the event is occurred triggering the output, and determine that the output includes outputting sensitive data. The system may determine output data that does not include the sensitive data, send the output data to a device, and may request the user to provide an authentication input to receive the sensitive data.