Anonymized Data Risk Assessment for Re-Identification Control
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Solution Overview
Problem
Existing methods lack a quantitative protocol for assessing the risk of re-identification in anonymized data, making it difficult to balance data utility with privacy concerns and comply with regulatory requirements.
Innovation Solution
An automated method using log-linear regression and risk assessment algorithms to calculate probabilistic risks and adjust the anonymization process to quantify and adjust the anonymization process, incorporating real-time risk assessment to optimize the balance between data utility and privacy, and adapt to different regulatory environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anonymized data is modified to make malicious analysis difficult, then data protection is improved, but data quality deviates greatly from real data
Solution Approach 1:
The patent applies parameter changes by systematically modifying data characteristics through anonymization techniques (generalization, suppression, perturbation) and using log-linear regression to adjust parameters that control the balance between protection and quality. The method dynamically tunes anonymization parameters to achieve optimal risk reduction while maintaining data utility.
Solution Approach 2:
The patent implements feedback mechanisms by calculating re-identification risk metrics and using these calculations to iteratively adjust anonymization parameters. The log-linear regression model provides feedback on the relationship between anonymization intensity and data quality loss, enabling continuous optimization of the protection-quality balance.
2Ease of manufacture
If existing anonymization algorithms are used without risk assessment, then data processing is simple, but the risk of re-identification cannot be quantified
Solution Approach 1:
The patent replaces simple anonymization algorithms with a comprehensive risk assessment system based on log-linear regression. This substitution transforms the approach from purely mechanical data modification to a statistically-grounded method that quantifies re-identification risk while maintaining automated processing capabilities.
Solution Approach 2:
The patent performs preliminary risk assessment calculations before finalizing anonymization parameters. By预先 calculating re-identification risks using log-linear regression on the anonymized data, the system determines appropriate anonymization levels and modifies data accordingly, ensuring both simplicity and precise risk quantification.
3Adaptability or versatility
If theoretical risk definitions are provided without calculation procedures, then regulatory guidance is comprehensive, but practical risk assessment cannot be performed
Solution Approach 1:
The patent enables self-service risk assessment by providing automated calculation procedures that organizations can implement independently. The log-linear regression framework and associated algorithms allow practitioners to perform comprehensive risk assessments without requiring external expert intervention, making regulatory compliance practical and accessible.
Data Source
Figure 1
AI summary
A method for improving data protection in a dataset (100) to be k-anonymized. Postanonymization, the reidentification risk is assessed (1000) by calculating the maximum risk from individual assessments (1010). This includes: calculating the inverse of the k-anonymity level as the risk of individual reidentification (1000); assessing attribute reidentification (1200) by identifying repeated attribute aggregations (1220) in the dataset, thereby calculating a risk for each record (1230) and deducing the maximum risk for attribute disclosure (1240); and determining inference reidentification risk (1300) by fitting (1320) the appropriate probability distribution to each attribute, applying log-linear regression (1340) to the data divided into two parts, and estimating the regression's predictive accuracy (1350). A weighted risk based on this accuracy is then calculated (1360) and the highest risk value is obtained. The maximum of all these risks (1900) defines the aggregate reidentification risk (2000), output to be compared against a predefined risk threshold.