Distributed Analytics Nodes for Secure Data Processing
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Solution Overview
Problem
Current systems face challenges in managing and analyzing data containing personally identifiable information or confidential data, as stringent privacy laws require data sanitization and secure transmission, leading to congestion and limitations in performing desired analytics.
Innovation Solution
A distributed computational analytic sharing architecture is implemented, where each analytics node manages access to its local data store, allowing for specific security and privacy policies to be enforced. Instead of transferring sensitive data, the architecture vets and transmits analytics requests, ensuring that sensitive information remains within the analytics node's purview and is sanitized from any generated results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensitive data is transmitted to users for analytics, then users can perform desired analytics, but network bandwidth becomes congested and privacy security is compromised
Solution Approach 1:
Instead of transmitting data to users for analytics, the patent inverts the approach by bringing analytics to the data through distributed computing nodes. Users submit analytics requests that are executed locally at data sources, and only results are returned. This eliminates the security risk of transmitting sensitive data while maintaining analytics capability.
Solution Approach 2:
The patent introduces distributed computing nodes as intermediaries between users and data sources. These nodes receive analytics requests from users, execute them against local data, and return results. This intermediary layer prevents direct data transmission while enabling analytics functionality.
2Object-affected harmful factors
If data is sanitized before transmission, then privacy security is improved, but network bandwidth is still congested due to multiple transmissions
Solution Approach 1:
The patent applies preliminary action by having distributed computing nodes prepare and execute analytics locally before any data transmission occurs. Data is processed in-place at the source, eliminating the need for multiple transmission cycles between sanitization and analysis, thus conserving network bandwidth.
3Loss of energy
If analytics are performed on local data without transmission, then network bandwidth is conserved, but users cannot access multiple data sources efficiently
Solution Approach 1:
The patent implements universality by designing distributed computing nodes that can handle multiple data sources and various analytics types within a unified framework. A single node can process different analytics requests across multiple local data sources, providing versatile analytics capability without requiring data transmission.
Data Source
AI summary
Described are systems and methods for securely distributing computational analysis across analytics nodes. In some embodiments, a system includes a plurality of analytics nodes with each analytics node managing access to its own data sources. The system includes an analytics controller configured to interact with a user to generate an analytic request including a selection of one or more analytics and a selection of one or more data sources determined based on the one or more selected analytics. The generated analytic request is transmitted to one or more analytics nodes corresponding to the one or more selected data sources. Each analytics node that receives the analytic request is configured to execute the analytic request to generate a result. The result from each analytics node of the one or more analytics nodes can be transmitted to the user issuing the analytic request.