Blockchain Entity Tracking via Inquiry Hash Matching
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Solution Overview
Problem
Sharing and retrieving information about entity behavior across organizations within a blockchain network is challenging due to restricted access and the need to avoid exchanging protected details, making it difficult to identify and notify entities of unwanted behavior.
Innovation Solution
A computer-implemented method that generates an inquiry hash from a set of attributes using a one-way function, allowing secure searching of a blockchain ledger without sharing attribute content, and assigns confidence ratings to match reports, enabling the identification of high assurance reports and additional information retrieval for low assurance reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If organizations share records containing entity behavior information, then information availability and collaboration improve, but data privacy and security are compromised due to exposure of protected details
Solution Approach 1:
The patent extracts only the essential identifying features (hashes of key attributes) from entity records while leaving out the full protected details. Organizations can search and compare these extracted hash identifiers to determine if records pertain to the same entity without exposing sensitive information, thus enabling information sharing while maintaining data privacy.
Solution Approach 2:
The patent introduces hash identifiers as an intermediary mechanism between organizations. Instead of directly sharing or comparing full entity records, organizations use these intermediary hash values to indirectly identify and match entities across different organizations' ledgers, enabling collaboration without direct exposure of protected details.
2Productivity
If organizations request and provide information about entity behavior, then tracking and monitoring capability improve, but the complexity of protected detail exchange increases
Solution Approach 1:
The patent extracts only the necessary identifying hash values from entity attributes, eliminating the need to exchange complex protected details. This reduction to essential identifiers simplifies the information exchange process while maintaining the ability to track and monitor entity behavior across organizations.
Solution Approach 2:
Instead of exchanging full entity records to enable tracking, the patent inverts the approach by having organizations publish hash identifiers of key attributes and searching for matches using these inverted identifiers. This reversal simplifies the tracking mechanism by avoiding complex direct exchanges of protected information.
3Measurement precision
If full entity attributes are shared across organizations, then identification accuracy improve, but the risk of unauthorized access and data breaches increase
Solution Approach 1:
The patent extracts hash identifiers from key entity attributes that are sufficient for accurate identification and matching purposes. These extracted hashes maintain identification accuracy by uniquely representing entities while containing no actual sensitive data, thus preserving reliability without exposing data to breaches.
Solution Approach 2:
The patent uses cryptographic hash copies of entity attributes instead of the original sensitive data. These hash copies serve as accurate representations for identification and matching while being one-way transformations that cannot be reverse-engineered to reveal the original protected information, thus maintaining both identification accuracy and data security.
Data Source
AI summary
A computer analyzes blockchain ledger content. The computer receives a set of attributes associated with a predetermined entity and applies a one-way function to the attributes, generating an inquiry hash. The computer receives access to a blockchain ledger that includes a report with at least one report hash associated with an attribute of a report entity. The computer searches the report using the inquiry hash as a search key. The computer generates a list of candidate reports containing hashes that matches the inquiry hash. The computer assigns an entity matching confidence rating to candidate reports based, at least in part, on a predetermined identification utility value associated with each inquiry hash matched. The identification utility value indicates a likelihood that the report entity is the predetermined entity. The computer generates a list of high assurance reports having entity matching confidence ratings above a predetermined assurance threshold.