Contextual Data Masking for Secure Private Data Linkage
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Solution Overview
Problem
Inspecting large volumes of data for insights is computationally intensive and transmitting sensitive data externally leaves it vulnerable to unauthorized access.
Innovation Solution
Implement contextual data masking and tokenization at the client side to anonymize and classify data, generating insights without exposing personally identifiable information (PII) by using client-specific encryption and secure servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If client data is transmitted externally for data inspection, then data insights can be generated, but sensitive data becomes vulnerable to unauthorized access
Solution Approach 1:
The patent applies preliminary action by performing data masking and tokenization before external transmission. The system masks sensitive fields and replaces them with tokens at the client side prior to sending data to external servers, ensuring that sensitive information is protected before it leaves the secure environment.
Solution Approach 2:
The patent uses an intermediary approach by introducing tokens as mediators between the original sensitive data and the external analysis system. These tokens preserve the ability to perform data linkage and analysis while acting as a protective layer that prevents direct exposure of sensitive information to external systems.
2Loss of information
If large volumes of data are retrieved and processed, then comprehensive insights can be obtained, but computational resources are heavily consumed
Solution Approach 1:
The patent extracts only the necessary information elements for analysis by masking unnecessary sensitive fields and retaining only the tokenized identifiers needed for data linkage. This extraction approach reduces the volume of sensitive data that needs to be processed while maintaining the capability to generate insights through token-based matching.
3Reliability
If sensitive data is protected through encryption and masking, then data security is improved, but data processing complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming sensitive data into tokenized form with different parameters (token identifiers instead of actual values). This transformation maintains the data's utility for linkage and analysis while changing its security parameters to protect sensitive information.
Data Source
AI summary
The present disclosure relates to methods and systems for contextual data masking and registration. A data masking process may include classifying ingested data, processing the data, and tokenizing the data while maintaining security/privacy of the ingested data. The data masking process may include data configuration that comprises generating anonymized labels of the ingested data, validating an attribute of the ingested data, standardizing the attribute into a standardized format, and processing the data via one or more rules engines. One rules engine can include an address standardization that generates a list of standard addresses that can provide insights into columns of the ingested data without externally transmitting the client data. The masked data can be tokenized as part of the data masking process to securely maintain an impression of the ingested data and generate insights into the ingested data.