Secure Co-Analysis Environment for Proprietary Data Sharing
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Solution Overview
Problem
Existing systems struggle to effectively analyze large volumes of proprietary data from multiple organizations to identify potential events of interest without revealing the underlying data to unauthorized parties, as organizations are hesitant to share sensitive information due to legal and competitive concerns.
Innovation Solution
A specialized computing environment with hardware and data security features allows competitive organizations to co-analyze proprietary data by storing it in volatile memory, performing analysis automatically, and sanitizing results to prevent data revelation, while using a secure enclosure that does not communicate wirelessly or have accessible ports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual keyword searches and conventional analysis methods are used, then data analysis can be performed with simple tools, but the analysis efficiency and ability to detect emerging threats is insufficient
Solution Approach 1:
The patent introduces a specialized computing environment that acts as an intermediary between multiple proprietary data sources and the analysis process. This environment includes a data reception module that receives data from multiple sources, a data processing module that analyzes the combined data, and a result output module that provides insights while maintaining data security. This intermediary structure enables efficient multi-source data analysis without requiring direct access to proprietary data, thereby improving productivity while managing complexity through modular design.
Solution Approach 2:
The system segments the data analysis process into distinct functional modules: data reception, data processing, and result output. Each module handles specific tasks independently, allowing the system to process large volumes of data from multiple proprietary sources efficiently. The segmentation also enables parallel processing and reduces the complexity burden on any single component, addressing both productivity and complexity concerns.
2Reliability
If proprietary data is shared for co-analysis, then comprehensive threat detection is improved, but competitive advantages and data security are compromised
Solution Approach 1:
The specialized computing environment serves as a secure intermediary that enables co-analysis of proprietary data without direct data sharing. Multiple organizations can input their proprietary data into the controlled environment, and the system processes this combined data to generate threat detection insights. The results are output in a form that provides comprehensive threat detection capability while preventing unauthorized access to the underlying proprietary data, thus maintaining competitive advantages and data security.
Solution Approach 2:
The patent creates a secure, controlled computing environment that acts as an inert atmosphere for data processing. Within this environment, proprietary data from multiple sources can be combined and analyzed without the risk of data leakage or unauthorized access. The environment is designed with strict access controls and security measures that prevent harmful factors such as data theft or misuse, enabling reliable threat detection while protecting data security.
3Ease of operation
If conventional keyword-based monitoring is used, then the system is easy to operate, but sensitivity and specificity for detecting unexpected events are limited
Solution Approach 1:
The patent replaces conventional mechanical keyword-based search methods with advanced data processing algorithms that can automatically identify patterns, anomalies, and emerging threats in proprietary data. The data processing module uses computational methods to analyze data structures, relationships, and trends without relying on pre-defined keywords, thereby significantly improving detection sensitivity and specificity while maintaining ease of operation through automated processing.
Data Source
AI summary
A specialized computing environment that includes hardware and data security features to enable competitive organizations to co-analyze proprietary data without revealing the underlying proprietary data to unauthorized users. Proprietary data are stored in volatile memory, which may be automatically erased according to pre-stored criteria. The analysis is performed automatically by a processing unit without human intervention. Analytical results are sanitized (e.g., using data masking) to prevent the analytical result from being tracible to any particular data source. Sanitized analytical results are output without outputting the underlying proprietary data (except to users authorized to validate analytical results). The computing environment is enclosed within a secure enclosure (e.g., a steel box with a lock), does not include any peripheral devices outside the secure enclosure, does not communicate wirelessly, and does not have hardware ports accessible from outside the secure enclosure (except, in some embodiments, a wired connection for a web server).