Reflected Data Access via Edge Nodes for Secure Local Profiling
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Solution Overview
Problem
Existing data profiling and quality assurance processes for large datasets are costly, time-consuming, and risk privacy breaches when handling sensitive data, especially when data sources are fragmented and require secure, local storage.
Innovation Solution
A distributed data access system that enables on-demand, real-time access to data through edge nodes, reflecting data to a unified user interface without storing it persistently outside the datastore, using end-to-end encryption and volatile memory storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensitive data is transferred to a network server for analysis, then data profiling and quality assurance can be performed, but the risk of privacy breach increases
Solution Approach 1:
The patent introduces an on-premise data analysis program as an intermediary that enables data profiling and quality assurance to be performed locally without transferring sensitive data to network servers. This mediator allows the entity to maintain data security while still achieving the desired analysis capabilities, resolving the contradiction between productivity improvement and privacy protection.
2Object-affected harmful factors
If an on-premise program is set up for local data analysis, then data security is maintained, but the setup time and cost increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring the data analysis capabilities within the on-premise program before data analysis is needed. The system is set up in advance with all necessary analysis tools and protocols, eliminating the need for time-consuming setup procedures when actual data profiling or quality assurance tasks need to be performed.
3Object-affected harmful factors
If data is stored locally without network access, then privacy is protected, but data profiling and quality checks become slower and more expensive
Solution Approach 1:
The patent enables the local system to serve itself by implementing comprehensive data profiling and quality assurance capabilities within the on-premise program. The system performs self-analysis without requiring external network resources, thereby maintaining data security while achieving efficient and cost-effective data analysis through autonomous local processing.
4Object-affected harmful factors
If data sources are fragmented across multiple datastores, then data security is maintained, but data quality analysis requires individual analysis of each datastore
Solution Approach 1:
The patent implements universality by designing the on-premise data analysis program to handle multiple different datastore formats and structures through a unified analysis framework. The system can perform data quality analysis across fragmented data sources using consistent methodologies, thereby maintaining data security while reducing the complexity of analyzing multiple disconnected datastores.
Data Source
AI summary
The present disclosure is directed to systems and methods for securely reflecting data from a local datastore. In one example embodiment, a user may connect local datastores to edge nodes that may facilitate communication between a third-party data management hub and the local datastores. A user operating the third-party data management hub may request to read/write certain data at the local datastores (that the user owns). Data from the local datastore may be generated in response to the request. The responsive data may be transmitted over an encrypted data session to the data management hub, where the responsive data is reflected to the user on a user interface. The responsive data is stored in a volatile memory store and not moved from the persistent memory at the local datastore. Upon termination of the encrypted data session, the volatile memory store is erased to preserve the privacy/security of the underlying data.