Hash-Based Data Anonymisation That Preserves Dataset Utility
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Solution Overview
Problem
Existing data anonymization techniques often compromise data utility by completely removing personal information, making it difficult for organizations to process and share datasets while ensuring compliance with privacy regulations.
Innovation Solution
A method involving combining original data values with a seed value, generating a hash, and using characters from the hash to create replacement values, which are then used to replace the original data, maintaining data utility while ensuring anonymization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personal data is completely removed from a dataset through anonymisation, then data privacy protection is improved, but data utility deteriorates making it difficult to process and share
Solution Approach 1:
The patent creates a replacement value that copies the essential characteristics and format of the original personal data while containing no actual personal information. The replacement value maintains the same data type, length, and structure as the original, allowing systems to process the anonymized data as if it were real data, thus preserving data utility while protecting privacy
Solution Approach 2:
The patent transforms the original personal data by changing its fundamental parameters - converting identifiable information into non-identifiable replacement values while maintaining structural parameters such as data format, length, and type. This allows the data to retain its functional properties for processing and analysis without containing any actual personal information
2Reliability
If data is anonymised to protect personal information, then compliance with privacy regulations is improved, but the ability to analyse and process the data set deteriorates
Solution Approach 1:
The replacement values are designed to copy the structural and functional properties of the original data without containing personal information. This allows existing data processing systems and analytical tools to continue operating on the anonymized data without modification, maintaining productivity while ensuring regulatory compliance
Solution Approach 2:
The anonymization method creates a universal solution that works across different data types and processing systems. The replacement values maintain compatibility with various data processing operations, allowing the anonymized dataset to be used for multiple purposes including analysis, testing, and sharing while meeting compliance requirements
3Reliability
If replacement values are generated using cryptographic hash functions, then anonymisation security is improved, but computational complexity increases
Solution Approach 1:
The patent pre-generates and stores a comprehensive set of replacement values before the actual anonymization process. This preliminary action creates a ready-to-use pool of replacement values that can be quickly assigned during anonymization, significantly reducing the computational complexity and processing time required during the actual data anonymization operation while maintaining strong security through the use of cryptographic hash functions for generation
Data Source
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AI summary
The present invention relates to computer-implemented data anonymisation comprising combining an original value with a seed value to form a combined value, processing the combined value to provide an output in the form of a string of characters, using characters from the hash string to determine a replacement value. The invention provides anonymised data sets which have retained utility relating to their intended purpose or function.