Anonymization Engine for Privacy Data Operations
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Solution Overview
Problem
Current data management systems face challenges in maintaining confidentiality and facilitating cross-sector collaboration due to stringent privacy regulations like HIPPA and FERPA, which hinder the easy sharing and analysis of personal identifiable information (PII) across different organizations.
Innovation Solution
A method involving administration engines on remote processing devices coupled with an anonymization engine that replaces PII with unique identifiers, allowing for secure data sharing and analysis while maintaining privacy compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high degree of privacy protection is implemented for PII, then confidentiality and security are improved, but data sharing and cross-sector collaboration are hindered
Solution Approach 1:
The patent introduces an anonymization engine as an intermediary component that sits between data sources and data consumers. This engine performs the critical function of removing PII from datasets while preserving contextual information, thereby enabling secure data sharing without compromising privacy. The intermediary resolves the contradiction by allowing data to flow between sectors while maintaining confidentiality through automated PII removal.
Solution Approach 2:
The patent extracts and removes PII from the data streams through the anonymization engine before data sharing occurs. By taking out the harmful element (PII) from the data while retaining the useful contextual information, the system enables cross-sector collaboration without exposing sensitive personal information. This extraction approach allows data to be shared safely across organizational boundaries.
2Reliability
If PII is removed from data sets, then privacy compliance is improved, but ability to cross-index datasets for research is severely limited
Solution Approach 1:
The patent applies local quality by differentiating between different types of data elements. PII is removed from the data while contextual information about individuals, organizations, and time periods is preserved. This selective approach maintains privacy compliance while retaining the necessary information for cross-dataset analysis and research correlations.
Solution Approach 2:
The anonymization engine creates a copy of the original data set with PII replaced by placeholders or anonymized identifiers. This copied data structure maintains the relationships and contextual information needed for research while eliminating the actual PII. The copy allows cross-sector analysis without exposing sensitive personal information to external systems.
3Adaptability or versatility
If data from multiple sources is collected for analysis, then research capability is improved, but data compatibility and integration issues increase
Solution Approach 1:
The anonymization engine provides universal functionality across multiple data sources and sectors. It handles various data formats and structures from different organizations (healthcare, education, corrections) through a single unified process. This multi-functional approach simplifies data integration by applying the same anonymization and standardization logic across all data sources, reducing overall system complexity.
Data Source
AI summary
A method for a plurality of administration engines on remote processing devices coupled to an anonymization engine operable to replace personally identifiable information (PII) with a unique identifier, further coupling the administration engines to a server and transmitting queries to the administration engines and receiving results in response to the querying. Then associating the contextual information in response to the unique identifiers, and formatting the contextual information to ensure data compatibility. Certain embodiments may include academic and health information.