Selective Data Obfuscation and Compaction for Secure PII Storage
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Solution Overview
Problem
The rapid growth of data storage demand has outpaced the capacity to store it, leading to storage and transmission bottlenecks, especially with multimedia data, and existing compression methods are inadequate for efficient data management.
Innovation Solution
A system and method for scriptable selective obfuscation of records using a data obfuscation module to anonymize personal identifiable information (PII) and encode datasets, integrated with a blockchain database for transaction management and data compaction, allowing for secure and efficient data storage and access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is used to increase storage capacity, then storage efficiency is improved, but data integrity is degraded due to lossy compression or space savings are insufficient due to lossless compression limitations
Solution Approach 1:
The data is divided into sourceblocks that are individually encoded and stored in a database. Each sourceblock can be independently managed, allowing for selective compaction while maintaining the ability to reconstruct complete datasets without loss of integrity.
Solution Approach 2:
The system creates encoded copies of the original data in a compacted form stored in a database, while maintaining the ability to retrieve and reconstruct the complete original dataset. This allows space-efficient storage without sacrificing data integrity through lossless encoding.
2Quantity of substance
If physical storage capacity is increased to meet demand, then storage capacity is improved, but transmission bandwidth requirements increase proportionally
Solution Approach 1:
Data is segmented into sourceblocks that can be selectively encoded and transmitted. Only the compacted encoded data needs to be transmitted to the database, reducing bandwidth requirements while the full capacity is available for storage.
Solution Approach 2:
The system changes the parameter of data representation from original form to encoded form, achieving compacted storage that requires less transmission bandwidth while maintaining full storage capacity for the encoded datasets.
3Reliability
If personal identifiable information is fully anonymized to ensure security, then data security is improved, but data utility is reduced making search and access difficult
Solution Approach 1:
Selective obfuscation is applied only to PII fields while leaving other data fields in their original form. This allows the system to maintain security for sensitive information while preserving searchability and utility for non-sensitive data through partial anonymization.
Solution Approach 2:
The system uses an obfuscation dictionary as an intermediary layer between the encoded data and the original data. This dictionary maps obfuscated PII to original values, enabling secure storage while maintaining the ability to retrieve and access complete information when needed.
4Ease of operation
If complete datasets are stored to maintain data integrity, then data utility is improved, but storage space consumption increases
Solution Approach 1:
The complete dataset is divided into sourceblocks that are encoded and stored in a database. This segmentation allows for compacted storage of the complete dataset while maintaining the ability to retrieve and reconstruct the full utility of the original data.
Solution Approach 2:
The system changes the parameter of data storage from storing complete original datasets to storing encoded representations. This parameter change achieves space-efficient storage while the encoding scheme preserves the ability to reconstruct complete datasets for full data utility.
Data Source
AI summary
A system and method for scriptable selective obfuscation of records comprising a data obfuscation module configured to identify and perform data anonymization on personal identifiable information (PII) contained within a plurality of records to create a partially-blurred dataset, and further comprising an encoder which receives the partially-blurred dataset and performs data compaction on the partially-blurred dataset before storing the compacted dataset in a data storage system. In some implementations, the data storage system is a blockchain database and the system functions as a clearinghouse to validate and monitor transactions involving data access rights between record owners and third-party entities. The system can further broker such transactions and direct payment form the third-party entity to the record owner when access rights have been purchased.