Pseudonymized Data Matching in Private Networks
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Solution Overview
Problem
Existing data sharing methods in private networks require the disclosure of personally identifiable information (PII), which compromises user privacy and increases the risk of data breaches, while also being inefficient and inaccurate due to reliance on probabilistic data matching.
Innovation Solution
A private, cloud-based, peer-to-peer network system that uses pseudonymized household identifiers to facilitate accurate data matching among data owners without sharing personally identifiable information, employing deterministic data matching and advanced cryptography to ensure security and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personally identifiable information is disclosed for data matching, then data matching accuracy is improved, but user privacy is compromised and data breach risk increases
Solution Approach 1:
The patent extracts and removes personally identifiable information from the data matching process. Instead of using actual PII for matching, the system uses pseudonymized identifiers that are derived from but do not contain the original sensitive data. This extraction of harmful elements (PII) allows accurate matching to continue while eliminating the privacy risk.
Solution Approach 2:
The patent introduces pseudonymized identifiers as an intermediary between the original PII and the data matching process. These intermediate identifiers serve as a mediator that enables accurate data matching while preventing direct exposure of sensitive personal information. The pseudonymized identifiers act as a bridge that maintains matching accuracy without compromising user privacy.
2Adaptability or versatility
If personally identifiable information is shared among data owners, then data matching capability is improved, but the risk of data breaches and re-identification increases
Solution Approach 1:
The system extracts and removes the vulnerable element (PII) from the data sharing process. By using pseudonymized identifiers instead of actual personal information, the patent eliminates the security risk while preserving the functional capability of data matching across multiple data owners.
Solution Approach 2:
The patent creates and uses copies (pseudonymized identifiers) of the original data instead of sharing the actual sensitive information. These copied identifiers maintain the essential matching properties needed for data owner collaboration while being inherently safer and不可逆 (irreversible) in terms of privacy protection.
3Reliability
If trusted third parties are used for data matching, then data security management is improved, but processing time increases and match reconciliation complexity arises
Solution Approach 1:
The patent enables data owners to perform data matching independently using pseudonymized identifiers, eliminating the need for centralized trusted third parties. Each data owner can autonomously conduct matching operations using the standardized pseudonymized identifier format, significantly reducing processing time and eliminating match reconciliation issues while maintaining security through the pseudonymization mechanism.
Data Source
AI summary
A system and method may facilitate data matching between multiple data owners through the use of a common set of “pseudonymized” individuals or household identifiers without requiring any data owner to share personally identifiable information with any other party. Data owners may accurately match ID-level online and offline data across multiple platforms with other participants' online and offline data without requiring any party to disclose personally identifiable information about their users or households. This use of deterministic data, rather than probabilistic data or a combination of deterministic and probabilistic data, is more accurate while also lowering the risk of data breach since no personally identifiable information is shared. Likewise, match processing time is greatly reduced through automation in a permissioned peer-to-peer network where each peer pseudonimizes its customer data with a universal or household ID to facilitate data matching with similarly pseudonimized customer data of other peers.