Loren Data's SAM Daily™

SAMDAILY.US - ISSUE OF MARCH 27, 2024 SAM #8156
SPECIAL NOTICE

70 -- INL Innovation Spotlight Innovative Data Concealment for Secure AI Research: The DIOD Methodology

Notice Date

3/25/2024 11:17:07 AM
 

Notice Type

Special Notice
 

NAICS

518210 — Data Processing, Hosting, and Related Services
 

Contracting Office

BATTELLE ENERGY ALLIANCE�DOE CNTR Idaho Falls ID 83415 USA
 

ZIP Code

83415
 

Solicitation Number

BA-1310
 

Response Due

3/25/2026 8:00:00 AM
 

Archive Date

04/09/2026
 

Point of Contact

Andrew Rankin
 

E-Mail Address

andrew.rankin@inl.gov
(andrew.rankin@inl.gov)
 

Description

INL Innovation Spotlight Innovative Data Concealment for Secure AI Research: The DIOD Methodology The DIOD methodology offers a groundbreaking approach to share critical data for AI research, ensuring confidentiality while maintaining data utility. Overview:��� �� In the era of big data, it is crucial to share information across platforms and organizations for innovation, especially in fields like AI research. However, the risk of sensitive data being reverse-engineered or compromised poses a significant challenge. Traditional data anonymization techniques often fall short, either by limiting data utility or failing to fully protect against data breaches. The DIOD (Deceptive Infusion of Data) methodology emerges as a solution, particularly relevant for industries where data sharing is essential yet risky, such as defense, healthcare, and energy. Its market potential is vast, considering the increasing reliance on AI for materials discovery, energy optimization, and security. Description:��� The DIOD methodology is an innovative approach to data sharing that successfully hides the identity of the system from which data originates, while still maintaining the functional dependencies required for AI research. It employs a non-invertible process to introduce deception into the data, ensuring the confidentiality of the original system's governing laws. Unlike traditional methods that can often degrade data quality or provide incomplete protection, DIOD preserves the crucial correlations needed for AI analysis. This enables researchers to utilize the data without jeopardizing the exposure of proprietary information. Benefits:��� ������ Enhanced Security: Enables data sharing while protecting sensitive system information. Preserved Data Utility: Maintains crucial correlations and functional dependencies for AI research. Scalability: Provides an efficient solution for different data sizes and types. Compatibility: Applicable across various scientific and industrial sectors without compromising data integrity. Innovation in Anonymization: Represents a significant advancement beyond traditional data protection methods such as k-anonymity and encryption. Applications:�� � Defense and Military: Facilitating secure sharing of data related to new technologies, while maintaining the confidentiality of critical information. Healthcare: Enabling the sharing of patient data for research purposes, while ensuring the full protection of personal information. Energy Sector: Facilitating the exchange of data on energy generation and storage innovations, while safeguarding proprietary processes. AI and Machine Learning Research: Providing benchmark datasets for the development and testing of AI algorithms, without any concerns regarding the origin of the data. Development Status:� Technology Readiness Level (TRL) 1: Basic principles observed and reported. IP Status: ������� Provisional Patent Filing No. 63/515,835, �Systems and Methods for Objective Management,� BEA Docket No. BA-1494. INL Tech Partnerships: Your Gateway to Innovation INL offers strategic access to proprietary technology, enhancing small business growth and contributing to economic and public advancement. We cater licensing terms to each business we work with, ensuring mutually beneficial agreements. Engage with our diverse technology offerings to propel your company forward. Learn more about our licensing opportunities and the support we provide at https://inl.gov/technology-deployment/. For specific discussions on how your business can benefit, please contact Andrew Rankin at td@inl.gov.
 

Web Link

SAM.gov Permalink
(https://sam.gov/opp/2ae7e0cfae6f44d7b1222f01f3ee7dcd/view)
 

Place of Performance

Address: Idaho Falls, ID 83415, USA

Zip Code: 83415

Country: USA
 

Record

SN07007465-F 20240327/240325230050 (samdaily.us)
 

Source

SAM.gov Link to This Notice
(may not be valid after Archive Date)

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