NIST
AI Research & Innovation
The challenge our customer brought us.
Deidentification algorithms (DEID) are susceptible to the same bias and privacy issues that impact other data analytics and machine learning applications. NIST is attempting to create a system that enables the evaluation and risk assessment of DEIDs.
What we designed and delivered.
Research DEID vulnerabilities and bias to better understand how to mitigate them.
Create a benchmarking effort hosted by NIST to compare deidentification methods.
Develop a comprehensive open-source evaluation suite for diagnosing failure points of generative AI and other deidentification techniques on tabular data, with focus on artifacts and bias issues that arise for diverse populations.
Compare the performance of differential privacy with traditional privacy techniques on both privacy and utility.
The difference it made.
450+
synthetic data samples shared publicly
This effort has collected, evaluated, and publicly shared over 450 synthetic data samples from academia, industry, and government organizations across the world.
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Other programs we’ve delivered.
Next Generation Threat System (NGTS) GPT
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1
Generative AI Logistics for Communication and Execution (GAILFORCE)
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2
Augmenting CILEMP to Enable Fleet Autonomy with Generative AI (CILEMP-FLAG)
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3
Goal Reasoning for Satellite Constellation Decision Making (GRANITE)
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4
Deep Reinforcement Learning
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5
AI-Powered Multi-Asset, Multi-Domain Operations Planning and Replanning Software (AMORE)
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6
Minefield Planning Evaluation and Gaming Suite
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7
Information Technology Services (LITeS) III
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8
Cyber Warning and Operations Center (CWOC) Research and Analysis
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9
Intelligence, Surveillance, and Reconnaissance Operations (ISRO)
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10
All Source Intelligence Cyberspace II
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11
Continuous Interactive Learners for Mission Planning (CILEMP)
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12
DARPA Explainable AI
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13
U.S. Census Disclosure Avoidance System
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15
NAVAIR Next Generation Threat System (NGTS)
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