Big Data Tokenization Using Title and Content Hash Verification
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Solution Overview
Problem
Existing tokenization methods struggle with protecting big data, particularly cleartext data, from unauthorized disclosure and breaches, especially when dealing with complex formats like PDFs and JPGs, as they are not parsed effectively.
Innovation Solution
Generate a token using an electronic file's title and content hashes, combined with a cryptographic signature, to create a secure and verifiable token representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional tokenization methods are used on cleartext big data, then data protection is provided, but the methods fail to effectively parse and protect complex formats like PDFs and JPGs
Solution Approach 1:
The patent segments big data into discrete tokenizable units by parsing complex formats (PDFs, JPGs, etc.) into their constituent elements. This segmentation allows traditional tokenization methods to be applied effectively to each parsed component, resolving the incompatibility between traditional methods and complex formats while maintaining data protection reliability.
2Ease of operation
If cleartext big data is stored and processed, then data accessibility and usability are maintained, but the risk of unauthorized disclosure and data breaches increases significantly
Solution Approach 1:
The patent introduces tokens as intermediary representations of cleartext data. These tokens maintain the functionality and accessibility of the original data while eliminating the security risks associated with storing and processing actual cleartext big data. The tokens serve as safe mediators that enable data operations without exposing sensitive information.
Solution Approach 2:
The patent creates token copies that represent the original cleartext data without containing the actual sensitive information. These token copies maintain the structural and functional properties needed for data processing and accessibility, while the irreversible nature of tokenization ensures that the original sensitive data cannot be reconstructed, thus eliminating unauthorized disclosure risks.
Data Source
AI summary
The present disclosure is directed to systems, methods, and non-transitory computer-readable media including generating a token using an electronic file, the electronic file having a title and a content, and the token including a title hash based on the title of the electronic file and a file hash based on the content of the file and verifying the token based on at least one of the title hash, the file hash, and the signature.