RPI ID: 2021-048-201

Innovation Summary:
This invention introduces a system for generating synthetic networks that replicate the structural properties of real-world covert or anonymized networks. It uses anonymized input data—such as node lists, edge lists, and group affiliations—to estimate the probability of connections between nodes. These probabilities are then used to generate synthetic networks that preserve the statistical and group structure of the original data. The system is designed to support research and analytics in sensitive domains without compromising privacy or security.

Challenges / Opportunities:
Analyzing covert or sensitive networks (e.g., criminal, terrorist, or confidential organizational networks) is difficult due to privacy concerns and limited access to real data. This invention addresses the need for realistic, privacy-preserving synthetic datasets that can be used for algorithm development, training, and testing. It opens opportunities for advancing network science, cybersecurity, and intelligence analytics without exposing sensitive information.

Key Benefits / Advantages:
✔ Privacy-preserving: uses anonymized data to generate realistic networks
✔ Statistical fidelity: maintains group and structural properties of original networks
✔ Flexible modeling: supports various network types and configurations
✔ Research-ready: enables safe experimentation and algorithm testing

Applications:
• Covert network analysis
• Cybersecurity and threat modeling
• Social network research
• Intelligence and defense analytics

Keywords:
#Syntheticnetworks #covertanalytics #anonymizeddata #networkmodeling #privacypreservingsimulation

Intellectual Property:
US Issued Patent 12,047,243