INL Innovation Spotlight Innovative Data Concealment for Secure AI Research: The DIOD Methodology
ID: BA-1310Type: Special Notice
Overview

Buyer

ENERGY, DEPARTMENT OFENERGY, DEPARTMENT OFBATTELLE ENERGY ALLIANCE–DOE CNTRIdaho Falls, ID, 83415, USA

PSC

IT AND TELECOM - PLATFORM PRODUCTS: DATABASE, MAINFRAME, MIDDLEWARE (HW, PERPETUAL LICENSE SOFTWARE) (7H20)

Original Source

https://sam.gov/opp/2ae7e0cfae6f44d7b1222f01f3ee7dcd/view
Timeline
    Description

    Special Notice: ENERGY, DEPARTMENT OF is seeking innovative data concealment for secure AI research using the DIOD methodology. The DIOD methodology offers a groundbreaking approach to share critical data for AI research while ensuring confidentiality. It is particularly relevant for industries such as defense, healthcare, and energy where data sharing is essential yet risky. The DIOD methodology successfully hides the identity of the system from which data originates while maintaining the functional dependencies required for AI research. It provides enhanced security, preserved data utility, scalability, compatibility, and represents a significant advancement in anonymization. The applications include defense and military, healthcare, energy sector, and AI and machine learning research. The technology is at Technology Readiness Level (TRL) 1 and has a provisional patent filing. INL Tech Partnerships offer strategic access to proprietary technology and licensing opportunities. For more information, contact Andrew Rankin at td@inl.gov.

    Point(s) of Contact
    Andrew Rankin
    andrew.rankin@inl.gov
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