The Department of Homeland Security Science and Technology Directorate (S&T) recently announced four contract awards, totaling $789,280, for the development of synthetic data capabilities that model and replicate the shape and patterns of real data.
“Sharing sensitive data that can be used for analytics, testing, and training machine learning models across organizational boundaries is highly challenging,” Melissa Oh, S&T’s Silicon Valley Innovation Program managing director, said. “Awarding these contracts are vital because startups are uniquely positioned to offer agile, creative approaches that can help the Department address complex challenges like data privacy and security in groundbreaking ways.”
California-based Rockfish Data was awarded to develop a high fidelity and privacy-preserving generative data platform that automatically adapts to diverse operational datasets.
Massachusetts-based DataCebo was awarded $196,920 to creating artificial intelligence (AI)-generated synthetic data using its Synthetic Data Vault E platform.
Austria-based MOSTLY AI was awarded $196,800 to create highly accurate and private tabular synthetic data to support model training, analytics and testing use cases.
Singapore-based Betterdata was awarded $196,260 to generate synthetic data that is statistically accurate when real data is unavailable or low in volume.
Awardees may be eligible for up to $1.7 million throughout four program phases.