Anonymized Dataset Risk Assessment Using Log-Linear Regression
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Solution Overview
Problem
Existing methods lack specific procedures or protocols for quantitatively assessing the risk of re-identification in anonymized data, despite the understanding of the associated dangers, leading to a notable absence in practical scenarios.
Innovation Solution
An automated method using log-linear regression and SUDA algorithms to calculate probabilistic risks of re-identification, allowing for real-time adjustment of anonymization processes to balance 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 attributes through anonymization techniques (generalization, suppression, perturbation) with controlled intensity. Different anonymization parameters can be adjusted to achieve optimal balance between protection and quality for specific use cases, allowing flexible tuning rather than fixed transformation rules
Solution Approach 2:
The patent implements dynamic evaluation by continuously assessing re-identification risk and data quality metrics. The system can adaptively adjust anonymization levels based on real-time risk assessments, allowing the protection mechanism to respond dynamically to different data types, users, and contextual requirements rather than applying static transformations
2Loss of information
If existing guides and regulations are used to define risk types, then theoretical understanding is provided, but quantitative assessment procedures are absent
Solution Approach 1:
The patent replaces manual, theoretical risk assessment with automated computational algorithms. Machine learning models and statistical methods automatically calculate re-identification probabilities, substituting human expert judgment with scalable computational systems that provide consistent, quantifiable results across different datasets
Solution Approach 2:
The system enables self-service by allowing organizations to autonomously assess and manage their own data protection risks without requiring external regulatory intervention. The automated tools provide immediate feedback on risk levels and recommended anonymization parameters, empowering users to make informed decisions independently
3Productivity
If automated risk assessment is implemented, then efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex risk assessment process into distinct modular components: data characterization modules, risk calculation modules, quality evaluation modules, and recommendation engines. Each module handles specific aspects independently, making the overall system more manageable, testable, and adaptable while maintaining high automation efficiency
Data Source
AI summary
A method for improving data protection in a dataset (100) to be k-anonymized. Post-anonymization, 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.