Automated Obscuring System for Sensitive Data Privacy
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Solution Overview
Problem
Existing systems fail to effectively obscure sensitive information during interactions between individuals, such as credit card numbers and personal details, from being disclosed to unauthorized parties in conversations, leading to potential data privacy breaches.
Innovation Solution
A computer-implemented method that processes content to predict the disclosure of sensitive information by converting non-text content into text, examining cues from both parties, and monitoring workflows to obscure sensitive information from platform users, using techniques like blocking content, providing filler audio, or garbled responses, and initiating alarms for superfluous information solicitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensitive information is fully disclosed to platform users for service delivery, then service efficiency is improved, but data privacy security deteriorates
Solution Approach 1:
The system performs preliminary analysis of conversation content to predict potential disclosure of sensitive information before it actually occurs. By examining cues from both the third-party and platform user, and monitoring workflow patterns, the system proactively identifies situations where sensitive information is about to be disclosed, allowing preventive obscuring actions to be taken before the breach happens
Solution Approach 2:
The system introduces an intermediary obscuring layer between the third-party and platform user. When sensitive information is detected or predicted, the system interposes obscured content (such as masked text or audio interruptions) in the communication stream, allowing the interaction to continue while preventing direct exposure of sensitive data to unauthorized parties
2Object-affected harmful factors
If content monitoring and analysis are performed to detect sensitive information, then data privacy protection is improved, but system complexity increases
Solution Approach 1:
The system employs machine learning models and automated analysis algorithms that enable self-service monitoring of conversation content. The system automatically processes audio and text inputs, identifies sensitive information patterns, and triggers appropriate obscuring actions without requiring manual review or intervention, thereby managing complexity through automation rather than human resources
3Object-affected harmful factors
If sensitive information is obscured from platform users, then data privacy security is improved, but service quality deteriorates
Solution Approach 1:
The system applies obscuring selectively and locally rather than globally. Only specific portions of content containing or leading to sensitive information disclosure are obscured, while the rest of the conversation flows normally. This localized approach ensures that service quality is maintained for non-sensitive portions of the interaction while protecting privacy where needed
Data Source
AI summary
A method, computer program product, and computing system for receiving content from a third-party. The content may be processed to predict the disclosure of sensitive information. The sensitive information may be obscured from a platform user, where the third-party may be a customer and the platform user may be a customer service representative.