Data Engine for Secure Centralized Storage
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Solution Overview
Problem
Existing data storage systems face challenges in protecting sensitive information while allowing for limited statistical analysis, as tokenization is computationally expensive and restrictive, and does not enable sorting, filtering, or basic statistical functions.
Innovation Solution
A fully customizable data engine performs extract, transform, and load (ETL) tasks to protect data security by accepting user-configured transformation functions based on data source, type, and significance, allowing for obfuscation, transformation, or removal of sensitive data while preserving its usability for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tokenization is used to protect sensitive data, then data security is improved, but computational cost increases and statistical analysis capability deteriorates
Solution Approach 1:
The patent segments data into different sensitivity levels (public, internal, confidential, restricted) and applies different transformation techniques to each segment. This allows critical data to be protected while less sensitive data remains accessible for analysis, resolving the contradiction between security and computational efficiency.
Solution Approach 2:
Different transformation functions are applied to different data fields based on their sensitivity level and analysis requirements. For example, numerical fields may use scaling transformations that preserve statistical properties, while text fields use masking. This local differentiation maintains security where needed while preserving analytical capability where appropriate.
2Reliability
If tokenization is used to protect sensitive data, then data security is improved, but the ability to perform statistical analysis deteriorates
Solution Approach 1:
The patent applies parameter transformations (such as scaling, offsetting, or aggregation) to numerical data that preserve statistical properties like mean, variance, and distribution shape while removing identifying information. This allows statistical analysis to proceed on transformed data without requiring access to original sensitive values.
Solution Approach 2:
The system creates transformed copies of sensitive data that retain analytical utility while removing security risks. These copies can be used for statistical analysis, machine learning training, and reporting without exposing actual sensitive values, thus preserving information needed for analysis while maintaining security.
3Ease of operation
If all data is stored in a centralized location for easy access, then data usability is improved, but security risk increases exponentially
Solution Approach 1:
The patent implements a segmented data architecture where data is divided into multiple storage locations or data lakes, each containing specific types or sensitivity levels of data. This segmentation allows selective access to different data portions, maintaining usability for specific analyses while limiting the impact of any single breach.
Solution Approach 2:
The system introduces data transformation intermediaries that sit between data storage and access points. These intermediaries apply transformations (masking, aggregation, differential privacy) to data before it reaches users or systems, enabling accessible data usage while preventing direct exposure of sensitive information and reducing breach impact.
Data Source
AI summary
A computer-implemented method for extracting data from separate databases and loading the data into a central data store without introducing additional security vulnerabilities is disclosed. The method begins by receiving a configuration comprising one or more transformation functions to be applied to one or more data fields. Values of the one or more data fields are extracted from the databases for a plurality of records, the values being previously tagged with metadata indicating at least a datatype for the values. Based at least in part on the configuration and the metadata tagging, a function from the one or more transformation functions is selected for each value. A second database with a central data store is populated with transformed data. Requestors are not permitted to access to pre-transformed data in the first one or more databases while being allowed access to at least some post-transformed data in the central data store.