Data Aggregation Hub for Privacy-Preserving Multi-Source Analysis
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Solution Overview
Problem
Current data collection and analysis methods face challenges in sharing and integrating data across multiple sources without compromising confidentiality, as existing anonymization techniques are often reversible, lose critical information, and require third-party intermediaries, which impedes the analysis of rare events.
Innovation Solution
A system and method where data agents operate at each data source, aggregating and processing data locally before forwarding summary results to a central hub for further analysis, ensuring confidentiality and allowing for comprehensive data analysis without exposing individual records or identities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is shared across multiple sources for comprehensive analysis, then statistical power and ability to detect rare events is improved, but confidentiality and privacy protection deteriorates
Solution Approach 1:
The patent segments the data analysis process into distributed data collection at source sites and centralized aggregate analysis at a hub. Individual data points remain at source sites while only aggregated results are transmitted, thereby maintaining statistical power through comprehensive data pooling while protecting privacy by preventing exposure of individual records
Solution Approach 2:
The patent introduces an intermediary aggregation system that acts as a mediator between data sources and analysis targets. The aggregation hub receives data from multiple sources, performs confidential analysis, and returns results without exposing individual data points, thus enabling comprehensive analysis while maintaining confidentiality
2Object-affected harmful factors
If anonymization techniques are applied to protect privacy, then confidentiality is improved, but data utility and analysis capability deteriorates
Solution Approach 1:
The patent inverts the traditional approach by not anonymizing individual data points before sharing, but rather by aggregating data at multiple levels before transmission. This inversion maintains full data utility for analysis while protecting confidentiality through aggregation, avoiding the information loss associated with traditional anonymization techniques
Solution Approach 2:
The patent adds the dimension of aggregation level to the data sharing process. By operating at multiple aggregation levels (local site aggregation and central hub aggregation), the system enables comprehensive analysis across the added dimensional layer while maintaining confidentiality, without losing information in the process
3Productivity
If data is centralized for comprehensive analysis, then analytical capability is improved, but trust requirements and complexity increase
Solution Approach 1:
The patent segments the centralized analysis function into distributed components at source sites and a coordinating hub. This segmentation maintains analytical capability by pooling data from all sources while reducing trust requirements and complexity by allowing each segment to operate semi-independently with clear defined interfaces
4Quantity of substance
If traditional data sharing methods are used, then data volume is improved, but control and security management deteriorates
Solution Approach 1:
The patent applies preliminary action by performing aggregation at source sites before data leaves the premises. This preliminary aggregation reduces the volume of data that needs to be managed and transmitted centrally, while maintaining the benefits of comprehensive data analysis, thus improving control and security management
Data Source
AI summary
The invention provides a system and method for automated data analysis in which data agents are located and operate at each member site or data source (i.e., locally). These agents access stored data at the data source or member sites, process the data and also aggregate the results. The aggregated results from each of the member sites are then forwarded to and further aggregated at a central analytic hub. The central analytic hub contains a centralized application which can further aggregate each of the aggregated results and perform a final analysis. These results are then delivered to the requester without any ability to identify individual data sources, or records from those sources.