Investigative Analysis System with Redacted Graph Sharing
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Solution Overview
Problem
Current data mining techniques are sub-optimal for complex investigative analyses involving data from disparate, incomplete, and inconsistent sources, especially in fields like intelligence analysis and police investigations, due to lack of human involvement and privacy concerns.
Innovation Solution
An investigative analysis system that allows users to share investigation results in the form of visual graphs, with the option to generate redacted versions omitting sensitive information, enabling human judgment on information leakage and relevance before sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated data mining techniques are used for investigative analysis, then processing speed and consistency are improved, but human judgment and adaptability are lost
Solution Approach 1:
The system segments the investigative analysis process into distinct phases: automated data mining for initial pattern detection, followed by human analyst review for judgment and adaptability. This allows both automated processing and human expertise to be utilized in their respective strengths without conflict.
Solution Approach 2:
The system introduces an intermediary layer between automated data mining and final investigative conclusions. This intermediary enables human analysts to review, validate, and adapt automated findings, bridging the gap between machine efficiency and human judgment.
2Loss of information
If complete investigation results are shared with other users, then information availability and collaboration are improved, but privacy concerns and information leakage risks increase
Solution Approach 1:
The system applies local quality by allowing different portions of investigation results to have different access permissions. Sensitive information can be restricted to specific users or groups, while non-sensitive information is freely shareable, enabling selective information disclosure that balances collaboration with privacy protection.
Solution Approach 2:
The system performs preliminary classification and permission assignment to investigation results before sharing. By pre-identifying sensitive information and setting appropriate access controls in advance, the system prevents information leakage while maintaining maximum possible information availability to authorized users.
3Quantity of substance
If data from many disparate sources is collected for complex investigations, then comprehensiveness and analytical depth are improved, but data consistency and completeness deteriorate
Solution Approach 1:
The system changes parameters by transforming heterogeneous data from disparate sources into a standardized format with consistent schemas, data types, and validation rules. This parameter transformation maintains the comprehensiveness of multi-source data while establishing reliability through uniformity.
Solution Approach 2:
The system introduces data integration intermediaries that mediate between disparate data sources and the investigative analysis engine. These intermediaries perform data cleaning, validation, and harmonization, ensuring consistency and completeness while preserving the breadth of information from multiple sources.
Data Source
AI summary
A computer-based investigative analysis system is disclosed in which a user can share results of an investigation with other users in a way that allows the sharing user to visualize how the results will be shared before they are shared. The results are shared in the form of a visual graph having nodes, edges, and other presentation elements. The nodes represent data objects that are the subject of the investigation and the edges represent connections between the data objects. The graph is shared in the form of an automatically generated redacted graph omitting nodes, edges, and presentation elements for which the other users do not have permission to view. Before sharing the graph, the sharing user is presented with a visualization of the automatically generated redacted graph providing the user an opportunity to confirm that sharing the redacted graph will not constitute an unauthorized information leakage.