DHS Awards 159K for Infrastructure to Prevent Credential Fraud
The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) has awarded $159,040 to Learning Machine Technologies, Inc. based in New York, to develop blockchain security technology to prevent credential fraud.
Government agencies issue, validate, and verify credentials for a variety of purposes. For example, DHS operational components, such as U.S. Customs and Border Protection, the Transportation Security Administration, and U.S. Citizenship and Immigration Services, issue, validate or verify eligibility requirements; licenses and certifications for travel, citizenship, and immigration status; employment eligibility; and supply chain security. Current processes are often paper-based, do not facilitate data exchange and use among systems, making them potentially susceptible to loss, destruction, forgery and counterfeiting. S&T is exploring the application of blockchain and distributed ledger technology (DLT) to issue credentials digitally to enhance security, ensure interoperability and prevent forgery and counterfeiting.
Learning Machine Technologies’ Phase 1 award project “Leveraging Learning Machine’s Commercial Offering in Public Infrastructure for Fraud Prevention” will adapt their current commercial technology using the open-source Blockcerts standard to support emerging global World Wide Web Consortium (W3C) security, privacy and interoperability standards such as decentralized identifiers (DID) and verifiable credentials for credential issuance and verification solutions. The proposed approach enables credential user and DID provider independence from vendor-specific accounts to access credentials and promotes holder control and interoperability.
“Standards-based interoperability is critical to implementing innovative, fraud resistant approaches to digital issuance of currently paper-based credentials.” said Anil John, S&T’s Silicon Valley Innovation Program (SVIP)Technical Director. “By adapting their existing platform to build support for emerging W3C global standards, Learning Machine will enable organizations to deploy solutions without vendor or platform lock-in concerns.”
The Phase 1 award was made under S&T’s SVIP Other Transaction Solicitation Preventing Forgery & Counterfeiting of Certificates and Licenses seeking blockchain and DLT solutions to fulfill common needs across DHS missions.
SVIP is one of S&T’s programs and tools to fund innovation and work with private sector partners to advance homeland security solutions. Companies participating in SVIP are eligible for up to $800,000 of non-dilutive funding over four phases to develop and adapt commercial technologies for homeland security use cases.