High-dimensional Data Anonymization via Attribute Compartmentation
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Solution Overview
Problem
Conventional anonymization methods for high-dimensional datasets often result in a significant loss of data quality, making it challenging to effectively anonymize data while preserving privacy and correlations.
Innovation Solution
The implementation of a data transformation technique called 'attribute compartmentation' that identifies unique attributes and modifies their combinations to ensure anonymity without altering the actual values, using a system architecture that includes an anonymization module integrated with a database management system to perform SQL operations and Bayesian network analysis for enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional anonymization methods are applied to high-dimensional datasets, then data privacy is protected, but data quality is dramatically lost
Solution Approach 1:
The patent applies segmentation by dividing the high-dimensional dataset into multiple lower-dimensional subspaces or projections. Instead of anonymizing the entire dataset at once, the data is split into manageable segments where anonymization can be applied more effectively, preserving local data quality while ensuring global privacy protection.
Solution Approach 2:
The patent transforms the high-dimensional data into lower-dimensional representations through projection techniques. By changing the dimensionality of the data space, the system maintains essential data characteristics and correlations while reducing the risk of re-identification and preserving data utility for analysis.
2Reliability
If data values are modified to ensure anonymity, then privacy is guaranteed, but correlations in the data are lost
Solution Approach 1:
The patent creates multiple copies or representations of the data in different projected spaces. By maintaining multiple views of the same underlying data structure, the system preserves correlations across different attribute combinations while ensuring that no single view reveals sensitive information that could compromise anonymity.
3Loss of information
If all attributes are retained in anonymized form, then data quality is maintained, but storage requirements and processing complexity increase
Solution Approach 1:
The patent applies different anonymization strategies to different subsets of attributes or data regions based on their specific characteristics. High-cardinality attributes may be projected more aggressively while low-cardinality attributes retain more detail, optimizing the balance between privacy protection and data utility without uniformly complicating the entire system.
Data Source
AI summary
A method and system are provided including at least one tabular data set, wherein each data set includes one or more attribute categories defining a record, each attribute category including one or more data values; an anonymization module; and an anonymization processor in communication with the anonymization module and operative to execute processor-executable process steps to cause the system to: receive a first data set at the anonymization module; determine at least one of the attribute categories in the first data set is a first-class identifier; generate a pseudonymous data set by removing the at least one first-class identifier attribute category from the first data set; determine at least two of the attribute categories in the pseudonymous data set are a second-class identifier; manipulate the at least two second-class attribute categories; and generate an anonymized data set based on the at least two manipulated second-class identifier attribute categories. Numerous other aspects are provided.