PII De-Identification Tool With Iterative Re-Identification Risk Control
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Solution Overview
Problem
Existing data de-identification methods are inefficient, subjective, and lack standardized methodologies, leading to inconsistencies, potential biases, and computational overhead, while risking re-identification and compromising data utility.
Innovation Solution
A data de-identification tool that automatically identifies direct identifiers, quasi-identifiers, and unique values within a structured dataset, applying transformation rules to convert them into de-identified data elements, while assessing and adjusting thresholds to minimize re-identification risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual techniques are used for data de-identification, then flexibility and customization are improved, but efficiency and consistency deteriorate due to substantial time consumption and subjectivity
Solution Approach 1:
The system performs automated de-identification without requiring manual intervention. The automated de-identification module scans datasets, identifies PII elements using entropy scoring and pattern recognition, and applies transformation rules automatically, eliminating the need for human operators while maintaining consistent application of de-identification standards across all data.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of human operators manually reviewing and de-identifying data, the system uses algorithmic approaches including entropy score calculations, regular expression patterns, and automated transformation rules to identify and de-identify PII elements, significantly improving efficiency while maintaining adaptability through configurable parameters.
2Productivity
If automated de-identification methods are applied, then efficiency and objectivity are improved, but data utility deteriorates due to loss of granularity and computational overhead
Solution Approach 1:
The system applies different de-identification transformation rules to different types of PII elements based on their specific characteristics. Direct identifiers receive one type of transformation, quasi-identifiers receive another, and unique values receive yet another approach. This localized treatment preserves data utility by applying the minimal necessary transformation to each element type rather than uniformly de-identifying all data.
Solution Approach 2:
The system dynamically adjusts de-identification parameters such as entropy thresholds and transformation rules based on the specific characteristics of the dataset being processed. By changing parameters adaptively rather than using fixed settings, the system optimizes the balance between de-identification effectiveness and data utility preservation for each unique data scenario.
3Reliability
If de-identification transformations are applied to protect privacy, then re-identification risk is reduced, but data value for analytical objectives deteriorates
Solution Approach 1:
The system applies partial de-identification by distinguishing between direct identifiers that require transformation and other data elements that can remain unchanged. By selectively applying transformations only where necessary for privacy protection rather than uniformly to all data, the system maintains data value for analytical purposes while still achieving adequate privacy protection for sensitive elements.
Solution Approach 2:
The system dynamically determines the appropriate level of de-identification transformation based on the specific PII element being processed. Rather than applying a static transformation level to all data, the system adapts its transformation approach based on the sensitivity, type, and context of each data element, optimizing the balance between privacy protection and data utility.
4Manufacturing precision
If standardized methodologies are implemented for data de-identification, then consistency and objectivity are improved, but complexity of implementation increases
Solution Approach 1:
The system segments the de-identification process into distinct modular components: entropy score calculation, PII element identification, transformation rule selection, and application. This segmentation allows each component to be independently configured and maintained, reducing overall implementation complexity while ensuring consistent application of standardized methodologies through structured processing steps.
Data Source
AI summary
The present disclosure is directed to methods and systems for data-driven de-identification tool that can detect personally identifiable information (PII) elements within a given dataset and apply selective mapping transformations rules to de-identify the dataset. The disclosed data de-identification tool identifies direct identifiers, quasi-identifiers and unique values within a structured dataset in an example embodiment. The data de-identification tool then transforms these potentially personal or sensitive data elements into de-identified data elements and replaces the identified direct identifiers, quasi-identifiers and unique values within the structured dataset with the de-identified data elements. The data de-identification tool calculates a risk of re-identification and based on the risk level, repeat the de-identification process iteratively until the risk levels are within an acceptable range.