Dynamic K-Anonymization System for Privacy-Utility Balance
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Solution Overview
Problem
Existing data anonymization methods lack robust automated tools for ensuring privacy protection, particularly in handling quasi-identifiers that can reveal personal information, leading to potential reidentification risks.
Innovation Solution
The implementation of a k-anonymization system that receives an input dataset, applies transformation steps to quasi-identifiers, checks anonymity values, and iteratively suppresses data to ensure that each record is indistinguishable from at least k-1 other records, using processors to generate a suppressed dataset with enhanced privacy protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data suppression and masking techniques are applied to protect privacy, then privacy protection is improved, but data utility and analytical value deteriorate
Solution Approach 1:
The system dynamically adjusts suppression parameters including the k-value threshold, suppression probability, and transformation intensity based on the assessed reidentification risk and desired data utility level. This allows optimization of the balance between privacy protection and data usefulness by changing the parameters of the suppression process rather than applying fixed suppression rules
Solution Approach 2:
The patent implements a dynamic k-anonymization process where the suppression strategy adapts iteratively based on feedback from anonymity value checks. The system dynamically selects which records to suppress in subsequent iterations based on their current anonymity values, creating a adaptive process that optimizes the privacy-utility tradeoff rather than applying static suppression rules to all records
2Reliability
If multiple transformation steps are applied to achieve k-anonymization, then privacy protection is improved, but processing complexity and computational resources increase
Solution Approach 1:
The system performs preliminary assessment of reidentification risk and preliminary transformation steps before final suppression. By evaluating anonymity values after each transformation step and planning subsequent suppression actions in advance, the system reduces the need for extensive iterative processing and optimizes the transformation sequence to achieve k-anonymization more efficiently
Solution Approach 2:
The patent divides the dataset into segments or groups based on quasi-identifier values and processes them in iterations. By segmenting the processing task and applying suppression selectively to specific groups that fail anonymity checks, the system reduces overall processing complexity compared to applying uniform transformation steps to the entire dataset
3Productivity
If automated tools are implemented for k-anonymization, then processing efficiency is improved, but implementation complexity and tool availability increase
Solution Approach 1:
The system implements automated self-assessment of anonymity values and self-adjustment of suppression strategies. The automated tool evaluates the dataset, identifies records failing k-anonymity requirements, and autonomously applies appropriate suppression transformations without requiring manual intervention, thereby improving processing efficiency while managing implementation complexity through automation
Data Source
AI summary
System and method for k-anonymization with a target k-value according to certain embodiments. For example, a method includes: receiving an input dataset; receiving a k-value, the k-value being a positive integer; receiving one or more quasi-identifiers corresponding to one or more data fields in the input dataset; receiving a data suppression strategy including one or more transformation steps, at least one transformation step of the one or more transformation steps associated with at least one quasi-identifier of one or more one or more quasi-identifiers; and applying the one or more transformation steps to the input dataset to generate a suppressed dataset including at least one suppressed data field corresponding to the at least one data field; checking an anonymity value of each data record of a plurality of data records in the suppressed dataset; selecting a subset of the suppressed dataset from the suppressed dataset.