Personal Data Association via Static Codes
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Solution Overview
Problem
Existing methods for linking personal and location data rely on master databases of personally identifiable information (PII), which poses significant risks and compliance challenges due to consumer privacy and security concerns.
Innovation Solution
A computer-implemented method that uses independent data stores with unique static codes and fuzzy searches to link personal and location data without storing PII, generating universal person and location codes to establish relationships between disparate data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a master database of personally identifiable information (PII) is used to link personal and location data, then data linkage accuracy is improved, but privacy and security risks increase
Solution Approach 1:
The patent extracts and removes personally identifiable information (PII) from the data linkage process entirely. Instead of using a master database containing names, addresses, and other PII, the system creates a PII-free environment where only anonymized data elements are stored and processed. This extraction eliminates the privacy and security risks associated with storing sensitive information while maintaining data linkage capabilities through alternative means.
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of probabilistic linkage algorithms and data element hashes that act as mediators between disparate data sources. Rather than directly comparing PII across databases, the system uses intermediate representations (hashes of data elements, probabilistic scores) that enable accurate linkage without exposing actual personal information. This intermediary layer preserves linkage accuracy while preventing direct access to sensitive data.
2Loss of information
If a master database of PII is used to link data, then complete personal profiles can be created, but compliance costs and processing complexity increase
Solution Approach 1:
The patent segments personal information into discrete, independent data elements (e.g., first name, last name, address components, date of birth) that can be processed and linked separately. Each data element is hashed and stored in independent tables, allowing the system to reconstruct complete personal profiles through aggregation of linked elements without requiring a centralized master database. This segmentation reduces compliance complexity while maintaining profile completeness.
Solution Approach 2:
The patent creates simplified copies of personal information in the form of hashed data elements and surrogate keys that replicate the linkage functionality of a master database without containing actual PII. These copied representations (hashes, anonymized identifiers) enable all necessary data matching and profile reconstruction operations while eliminating the need to store or process sensitive original data, thereby reducing compliance burdens.
3Reliability
If traditional PII-based linkage methods are used, then established data matching capabilities are maintained, but vulnerability to data breaches and regulatory penalties increases
Solution Approach 1:
The patent fundamentally changes the parameter representation of personal data by transforming readable PII into hashed, anonymized forms. Instead of storing and processing actual names, addresses, and social security numbers, the system operates on hash values and anonymized identifiers that maintain the mathematical properties needed for matching while eliminating the sensitivity of the original data. This parameter transformation preserves data matching reliability while removing vulnerability to breaches.
Solution Approach 2:
The patent converts the potential harm of data exposure into a benefit by deliberately anonymizing data elements before storage and processing. The hashing and anonymization processes, which initially seem to reduce data quality, actually create a system where data can be freely linked and analyzed without privacy risks. The apparent loss of direct identifiability becomes the protective feature that eliminates breach vulnerability while maintaining linkage accuracy through probabilistic methods.
Data Source
AI summary
A computer-implemented method of identifying an individual independently of the individual's personally identifying information includes providing independent data stores for elements of personal identifying information for a population and fuzzy searching the data stores independently for the elements. Each data store associates each element value and its known variations with a unique static code. The search returns the unique static code associated with each of the elements found and a new independent code is generated if no code is found. The returned 10 codes are concatenated to form a person code. The person codes link information to produce a relationship between disparate data without a master database of people and PII.


