Hash-Based Data Anonymization 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 anonymize the data, while maintaining utility by preserving non-personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from the identifying field is removed or anonymized, then personal data protection is improved, but data utility deteriorates
Solution Approach 1:
The patent creates a copy of the original data value and transforms it through hashing to generate a replacement value. The original identifying field data is replaced with this transformed copy, maintaining data structure and utility while eliminating personal information. The hash function creates a deterministic transformation that preserves relationships between records without exposing original personal data.
Solution Approach 2:
The patent changes the parameter state of the identifying field data by applying a hash function transformation. The original personal data values are converted into hash-based replacement values, changing their form from identifiable personal information to anonymized representations that maintain structural consistency for processing purposes.
2Reliability
If traditional anonymization methods are used to protect personal data, then privacy compliance is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent replaces complex mechanical or manual anonymization processes with a cryptographic hash function-based system. Instead of using traditional methods that may involve manual review, complex algorithms, or multiple processing steps, the invention uses hash functions to efficiently generate replacement values that ensure privacy compliance while maintaining processing speed and scalability.
3Object-affected harmful factors
If identifying field data is completely removed, then personal information exposure risk is reduced, but data set processability deteriorates
Solution Approach 1:
The patent introduces hash-based replacement values as an intermediary between the original personal data and the anonymized output. These replacement values serve as mediators that maintain the structural integrity and processability of the dataset while eliminating direct exposure to personal information. The intermediary values preserve relationships and patterns needed for processing without containing identifiable personal data.
Data Source
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.


