Semantic Knowledge Object Encoding for Privacy Compliance
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Solution Overview
Problem
Traditional database structures and schemas lack the ability to provide comprehensive data compliance information, restricting the use of metadata and data catalogs for data compliance tasks, and do not allow for the derivation of essential information required for data privacy and protection.
Innovation Solution
The use of knowledge objects (KOs) to represent and categorize data without retaining the underlying data, allowing for the mapping of compliance-related information and enabling enterprises to analyze systems for compliance issues and comply with data subject requests by encapsulating semantic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional database metadata and data catalogs are used for data compliance tasks, then data structure and schema information can be provided, but the ability to provide comprehensive compliance information is restricted
Solution Approach 1:
The patent introduces knowledge objects as an intermediary layer between traditional database metadata and compliance requirements. These knowledge objects encode semantic information about data categories, sensitivity levels, and compliance attributes, enabling the system to bridge the gap between structured database schemas and unstructured compliance information needs without losing critical compliance data.
Solution Approach 2:
The patent creates a copy of data information in the form of knowledge objects that represent data categories and compliance attributes without storing the actual underlying data. This copying mechanism allows compliance information to be derived and maintained separately from the original data structures, preventing information loss while maintaining adaptability to various compliance frameworks.
2Ease of operation
If traditional database schemas are used, then data organization and relational operations are facilitated, but comprehensive data privacy and protection information cannot be provided
Solution Approach 1:
The patent segments data representation into two distinct components: traditional database schemas that handle data organization and relational operations, and knowledge objects that encode data privacy and compliance information. This segmentation allows each component to fulfill its specific function without compromising the other, enabling both ease of relational operations and preservation of privacy information.
Solution Approach 2:
The knowledge objects serve multiple functions simultaneously: they encode data category information, sensitivity levels, compliance attributes, and privacy requirements. This multi-functionality allows a single data structure enhancement to address multiple information needs without disrupting existing database operations, thereby preventing loss of privacy information while maintaining operational ease.
3Quantity of substance
If metadata from traditional databases is used for compliance tasks, then basic data catalog information is available, but essential compliance-related information is not derivable
Solution Approach 1:
The patent creates a composite information structure that combines traditional database metadata with encoded knowledge objects. This composite approach integrates the quantitative data catalog information from traditional schemas with the qualitative compliance-related information from knowledge objects, ensuring that neither quantity nor quality of information is lost in the compliance analysis process.
Data Source
AI summary
A system receives a plurality of knowledge objects (KOs). The system receives repository structure definition information, the repository structure definition information specifying one or more repository structure definitions that define respective structures for the one or more data repositories. The system groups the plurality of KOs based on the name, type, and tag attributes of the KOs, and storage paths of the underlying unit of structured, semi-structured, and unstructured data at the one or more data repositories corresponding to the KOs to generate a number of groups of KOs. The system determines a number of compliance categories (CCs), each CC corresponding to a standard on data privacy or data protection compliance mandates. The system determines matching relationships between the CC to each group of KOs. The system generates a first mapping structure that maps relationships between each group of KOs to the CC based on the matching relationship.