Real-Time Patient Dataset Anonymization Using Cached Statistical Parameters
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Solution Overview
Problem
Conventional methods for anonymizing large volumes of patient datasets are time-consuming and resource-intensive due to the need for laborious data preprocessing, which is impractical with continuous data streams and slow data transfer speeds in medical databases.
Innovation Solution
A computer-implemented method that analyzes statistical population data to ascertain obfuscation parameters, allowing for the obfuscation of quasi-identifiers in patient datasets without preprocessing, enabling faster and more efficient anonymization using fewer resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional k-anonymity methods are used to anonymize patient datasets, then data anonymity is improved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing generalization limits for quasi-identifiers in a cache memory before actual anonymization processing. When a anonymization request arrives, the system retrieves pre-computed limits instead of calculating them from scratch, dramatically reducing processing time while maintaining k-anonymity guarantees
Solution Approach 2:
The patent implements partial action by selectively applying anonymization only to quasi-identifier attributes that require it, rather than processing entire datasets uniformly. The system identifies and anonymizes only the necessary attributes (age, gender, zip code) while leaving other data intact, reducing overall processing overhead
2Manufacturing precision
If data preprocessing is performed to obtain generalization limits for quasi-identifiers, then anonymization quality is improved, but the process becomes impractical for continuous data streams
Solution Approach 1:
The system performs preliminary computation of generalization limits based on statistical properties of the data (standard deviations, quartiles) and stores these in cache. This allows the system to handle continuous data streams by having anonymization parameters ready in advance, enabling real-time processing without blocking for preprocessing
Solution Approach 2:
The patent replaces the mechanical preprocessing step with a statistical modeling approach. Instead of manually or computationally intensive preprocessing of entire datasets, the system uses statistical descriptors (mean, standard deviation, quartiles) to derive generalization limits analytically, enabling scalable processing of continuous data streams
3Quantity of substance
If large volumes of patient data are stored in medical databases, then data availability for research is improved, but data transfer speeds become bottlenecked
Solution Approach 1:
The patent extracts only the essential statistical properties (standard deviations, quartiles, mean values) from large datasets and stores these compact representations. This extraction approach reduces data volume for processing while preserving the information needed to compute generalization limits, enabling faster handling of large datasets
Solution Approach 2:
The system creates and uses cached copies of computed generalization limits and statistical parameters. Instead of repeatedly accessing and processing the original large datasets, the system works with compact cached copies, dramatically reducing data transfer requirements and processing time
Data Source
AI summary
A computer-implemented method for providing anonymized patient datasets, comprises: analyzing statistical population data to ascertain obfuscation parameters; and anonymizing patient datasets including quasi-identifiers as attributes by obfuscating the quasi-identifiers of the patient datasets based on the obfuscation parameters to generate the anonymized patient datasets. A system includes at least one processor and a memory, and is configured to provide the anonymized patient datasets.