Audit Trail System for User Profile Data Privacy
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Solution Overview
Problem
Existing online social networks lack granular privacy controls and traceability, leading to issues with user consent management and consumer feedback utilization, resulting in user dissatisfaction and ineffective marketing strategies.
Innovation Solution
Implementing a system that creates audit trails of data incorporation by linking trust objects to user profiles, providing granular privacy controls, and measuring product reputation through consumer feedback analysis, allowing users to customize data access and recall opt-in actions, and enabling businesses to make data-driven decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If granular privacy controls are implemented with audit trails, then user privacy and trust are enhanced, but system complexity increases
Solution Approach 1:
The patent segments privacy controls into granular, field-level permissions rather than blanket controls. Each user profile field can have independent access controls and audit trails, allowing precise tracking of who accessed what data and when. This segmentation enables comprehensive privacy monitoring without requiring a complete system overhaul.
Solution Approach 2:
The patent introduces an intermediary audit trail system that mediates between data access requests and user privacy concerns. This intermediary layer records all data incorporations and access events, providing transparency without directly interfering with the core data processing operations.
2Reliability
If opt-in actions are tracked with circumstantial information, then customer repudiation is reduced, but data storage requirements increase
Solution Approach 1:
The patent performs preliminary recording of opt-in actions with circumstantial information (device type, location, referring URL) at the moment of consent. By capturing this contextual data upfront, the system creates robust evidence that prevents later repudiation without requiring continuous monitoring or additional storage during the consent period.
Solution Approach 2:
The patent creates copies of essential opt-in contextual information and stores them in the audit trail. Rather than storing entire session recordings or continuous data streams, the system captures and preserves only the critical contextual elements needed to verify consent validity, significantly reducing storage requirements while maintaining reliability.
3Productivity
If consumer feedback is systematically collected and analyzed, then business decision-making is improved, but processing time and resources increase
Solution Approach 1:
The patent implements a systematic feedback mechanism where consumer reviews and social media mentions are continuously collected, analyzed, and fed back to businesses. This closed-loop feedback system automatically processes unstructured consumer input and transforms it into actionable insights, improving decision-making quality while minimizing manual processing time through automated analysis.
Solution Approach 2:
The patent enables the feedback system to serve itself by implementing automated collection, processing, and analysis of consumer feedback. The system automatically crawls social media platforms, collects reviews, analyzes sentiment, and generates insights without requiring constant human intervention, thereby reducing processing time and resource requirements while maintaining high decision-making quality.
Data Source
AI summary
The technology disclosed relates to creating an audit trail of data incorporation in user profiles. In particular, it relates to linking trust objects to fields of the user profiles.The technology disclosed also relates to maintaining an opt trail that captures user opt-ins by recording the circumstances surrounding opt-in actions. In particular, it relates to linking trust objects to user profiles that connect users to an advertising campaign.The technology disclosed further relates to tracking and measuring reputation of product models in consumer markets. In particular, it relates to assembling consumer feedback on the product models from online social networks and service records of the product models and applying sentiment analysis on the consumer feedback.