Self-Governing Data Commingling for Zero-Trust Analytics Access
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Solution Overview
Problem
Conventional databases and data systems lack the ability to securely share and commingle sensitive data from multiple publishers while allowing each publisher to define and enforce granular access rules, preventing misuse and unauthorized access.
Innovation Solution
A decentralized system using homomorphic encryption and distributed ledger technology, combined with a Self-Governing data security mechanism, enables secure data sharing and commingling by allowing each publisher to define and enforce granular security policies on each data element, ensuring compliance with their rules, even in combinatorial analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional databases are used to store and share data from multiple publishers, then data storage and access are simplified, but security control and prevention of misuse are insufficient
Solution Approach 1:
The patent segments data into individual data elements, each with its own security policy attached. This allows granular control over each data element's access and usage, enabling publishers to define specific rules for each element rather than applying blanket security policies to entire datasets. The segmentation principle resolves the contradiction by making security control more精细 while maintaining ease of data access through automated policy enforcement.
Solution Approach 2:
The patent introduces an intermediary mechanism (the data element security policy and enforcement system) that sits between the data and users. This intermediary automatically enforces security rules, preventing direct access and potential misuse while allowing legitimate access. The intermediary resolves the contradiction by providing automated security control that does not complicate data access operations.
2Productivity
If data from multiple publishers is commingled for combinatorial analytics, then predictive accuracy and operational efficiency are enhanced, but risk of data misuse and unauthorized access increases
Solution Approach 1:
The patent applies preliminary action by attaching security policies to data elements before they are commingled for analytics. Publishers define and enforce security rules in advance, specifying how each data element can be used, accessed, and combined. This preliminary security configuration allows safe commingling of data for combinatorial analytics while preventing misuse, resolving the contradiction between enhanced predictive accuracy and reduced data misuse risk.
Solution Approach 2:
The patent changes the security parameter from coarse-grained (dataset-level) to fine-grained (data element-level). By attaching individual security policies to each data element, the system enables precise control over how data elements can be combined and used in analytics. This parameter change allows productive commingling of data while maintaining strict security controls, resolving the contradiction between productivity and harmful factors.
3Reliability
If granular security policies are enforced on each data element, then data protection and compliance are improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent implements self-service by making data elements self-governing through embedded security policies. Each data element carries its own security rules and can enforce its own protection requirements without requiring complex external validation. This self-service approach improves data protection while reducing system complexity by eliminating the need for centralized security management of each data element.
Solution Approach 2:
The patent applies preliminary action by pre-attaching security policies to data elements during creation or publication. This preliminary configuration eliminates the need for complex runtime security decision-making, as the security rules are already embedded and can be automatically enforced. This resolves the contradiction by improving data protection through granular policies while minimizing system complexity through automated enforcement.
Data Source
AI summary
A system supporting operations on securely commingling Self-Governing data sets from a plurality of Publishers is provided. Such a system performs storing, organizing and commingling Self-Governing data tuples that contain at least a data element cell value, a Publishers assigned Self-Governing data security element value, a unique identifier of the Publisher, and a datetime stamp, blocking direct user access to any Self-Governing data or Analytic Services during and after deposit into the system, blocking direct view of or access to any data cell in ways which violate cell-level Need-to-Know policies contained for every data element in every Self-Governing Data set, executing Analytics Services operations on the commingled data without exposing any data cell to any system user, separating access to results of the Analytic Services from the commingled Self-Governing data sets based on their Zero-Trust Self-Governing visibility policies immutably and persistently applied to the Self-Governing data at the data cell level.