Anonymizing Sensitive Data With Human-Readable Hash Tokens
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Solution Overview
Problem
Existing anonymization methods using cryptographic hash functions generate unreadable hash values that hinder understanding when displayed in user interfaces, and scaling these processes is challenging due to the need for maintaining and updating identifier lists.
Innovation Solution
A method and system that split secure hashed values into hash tokens, convert each token to human-readable tokens using a 1:1 process, and concatenate them to form semantically similar identifiers, facilitating easier understanding and scalable anonymization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptographic hash functions are used to anonymize sensitive data, then security is improved, but readability is worsened
Solution Approach 1:
The patent segments the hash value into multiple tokens and converts each token independently into a human-readable component. This segmentation allows the system to maintain the security benefits of hashing while improving readability by presenting processed, meaningful tokens instead of raw hash values.
Solution Approach 2:
The patent introduces an intermediary conversion process that transforms hash values into human-readable tokens. This intermediary layer acts as a mediator between the secure but unreadable hash values and the user interface, preserving security while enhancing readability through the conversion mechanism.
2Ease of operation
If identifiers are converted to human-readable format, then readability is improved, but process scalability is worsened
Solution Approach 1:
By segmenting the identifier conversion into independent token processing units, the system enables parallel processing of multiple identifiers simultaneously. Each token can be converted independently without requiring sequential access to a global counter, thus improving scalability while maintaining readability.
Solution Approach 2:
The conversion process is designed to be self-service and stateless, where each hash value can be independently converted to human-readable tokens without requiring coordination with other conversion operations. This eliminates the need for global counter management and enables the process to scale efficiently.
Data Source
AI summary
Sensitive data may be anonymized for use in user interfaces by applying a cryptographic hash function to the data. The hashed value may be broken into hash tokens and the hash tokens converted to human readable tokens using a 1:1 conversion function. The human readable tokens can then be concatenated together to provide a human readable identifier of the sensitive data.