Privacy Metadata Overlay in Distributed Data Objects
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Solution Overview
Problem
Complying with data privacy laws and regulations is labor intensive, cumbersome, and prone to error due to the lack of privacy classification in data structures, leading to potential mishandling of personally identifiable information (PII) across different applications and jurisdictions.
Innovation Solution
A data schema that overlays privacy classification as metadata on user data stored as digital objects in a computing system, allowing an administrator to define custom privacy classifications and propagate them across applications, ensuring compliance with data privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data is stored in traditional database records without privacy classification metadata, then storage simplicity is maintained, but data privacy compliance becomes labor intensive and error-prone
Solution Approach 1:
The patent embeds privacy classification metadata within the data structure itself, nesting privacy information inside digital objects. This allows privacy classification to travel with the data through processing pipelines without requiring separate tracking systems, resolving the contradiction between storage simplicity and compliance ease.
Solution Approach 2:
The patent introduces a privacy agent as an intermediary component that automatically detects, classifies, and tags PII in data structures. This mediator handles the complex privacy classification task, making compliance easier without burdening the storage system while maintaining automated privacy management.
2Measurement precision
If privacy classification is manually applied to data structures, then accurate privacy labeling is achieved, but processing time and labor intensity increase significantly
Solution Approach 1:
The patent enables data structures to self-identify PII through automated detection mechanisms. The system performs self-service privacy classification by automatically analyzing data content and applying appropriate privacy labels without manual intervention, maintaining high accuracy while improving processing speed.
Solution Approach 2:
The patent implements feedback loops where the privacy agent continuously monitors data processing activities, learns from classification patterns, and refines privacy labeling accuracy over time. This automated feedback mechanism maintains precision while eliminating manual labor bottlenecks.
3Reliability
If privacy metadata is propagated across multiple applications in distributed systems, then cross-application privacy compliance is ensured, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent creates a universal privacy metadata structure that can be propagated across different applications and systems. The standardized digital object format with embedded privacy classification serves as a multi-functional carrier that works across diverse processing environments, ensuring compliance reliability without requiring application-specific implementations.
Solution Approach 2:
The privacy agent acts as an intermediary that standardizes privacy metadata propagation across application boundaries. It translates and transports privacy information between different systems, ensuring consistent compliance while abstracting away the complexity from individual applications.
4Reliability
If comprehensive privacy tracking is implemented across all data processing activities, then regulatory compliance is improved, but computational overhead and resource consumption increase
Solution Approach 1:
The patent performs privacy classification and metadata attachment as preliminary actions during data ingestion and initial processing. By classifying PII upfront rather than continuously tracking throughout the entire data lifecycle, the system achieves comprehensive compliance coverage while minimizing ongoing computational overhead.
Data Source
AI summary
Computing systems, devices, and associated methods of privacy data management in a distributed computing system are disclosed herein. In one example, a method includes receiving a request from a data consumer for privacy information of data stored as data objects. The data objects are logically structured according to a data schema defining a property containing a property value representing user information and an annotation to the property containing a privacy classification of the property value of the property. The method also includes inspecting the data schema of the data objects to identify the privacy classification of the property and transmitting the identified privacy classification to the data consumer to allow the data consumer to configure an application to process the property value of the property in the data objects according to an organizational, legal, or regulatory processing requirement in one or more jurisdictions.