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McGirl, Natalie

Publications and source records attributed to McGirl, Natalie.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Enabling Computation on Sensitive Data in International Safeguards with Privacy-Preserving Encryption Techniques

Privacy-preserving machine learning is a field of study that explores how to protect and preserve the privacy of sensitive data while allowing the data to be used by machine learning algorithms. This field has had substantial industry investment due to heightened concerns about privacy in the technology industry, with a focus in two broad application areas: financial services and healthcare. Numerous privacy-preserving methods have also been proposed for international safeguards, but they have been difficult to enact because the data they require is con- sidered sensitive or proprietary by the nuclear facility operator. This work examines how current privacy-preserving approaches might be used to enable the International Atomic Energy Agency (IAEA) to use that data to contribute to a safeguards conclusion about a state while giving nuclear operators confidence that their sensitive data is adequately protected. This paper begins by exploring several broad categories of privacy-preserving techniques including homomorphic encryption, secure multiparty computation, secure enclaves, and zero-knowledge proofs. Then we discuss some of the security considerations related to using these methods, potential use cases, and a conceptual system design for applying privacy-preserving methods in international safeguards.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

A Conceptual Design for a Mobile Application to Support Infield Inventory Activities

This paper introduces an inventory assistant being developed at Oak Ridge National Laboratory (ORNL) that we believe will empower users to perform inventory activities at nuclear facilities more accurately, reliably, and quickly. Inventory activities at nuclear facilities are often conducted using pen and paper, which can be time-consuming, tedious, and susceptible to reading or transcription errors. The proposed inventory assistant would replace the paper-based process used by International Atomic Energy Agency (IAEA) inspectors, nuclear facility operators, or verification monitors to complete an inventory of nuclear and non-nuclear items. In general, the inventory assistant would ingest an inventory list, distribute assigned items from the inventory list to one or more mobile devices, enable inventory teams to record their observations in the field, and then enable an inventory lead to integrate and reconcile the observations to produce a final report. The assistant consists of two software components—one for the inventory teams to record observations in the field (In-Field Observations App [IFOA]) and one for the inventory lead to reconcile the inventory list with observations (Distribution, Integration, and Reconciliation Application [DIRA]). This paper introduces the overall workflow of the inventory assistant and describes the IFOA user experience in more detail. To demonstrate the concept, the authors present a use case of IAEA inspectors conducting item counting and tag checking activities of UF6 cylinders at a gas centrifuge enrichment plant with a large number of UF6 cylinders (e.g., thousands). These activities can currently require 30–40 person-days of inspection to complete. Based on experiences during an exercised performed at the IAEA by the ORNL team in 2016, we believe an inventory assistant could allow the IAEA to complete item counting and tag checking using the global identifier or the operator’s barcode in 8–10 person-days of inspection. We would expect other users (e.g., facility operators or verification monitors) to also benefit from significant time savings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2017

Early Results from the Advanced Radiation Protection Thick GCR Shielding Project

The Advanced Radiation Protection Thick Galactic Cosmic Ray (GCR) Shielding Project leverages experimental and modeling approaches to validate a predicted minimum in the radiation exposure versus shielding depth curve. Preliminary results of space radiation models indicate that a minimum in the dose equivalent versus aluminum shielding thickness may exist in the 20-30 g/cm2 region. For greater shield thickness, dose equivalent increases due to secondary neutron and light particle production. This result goes against the long held belief in the space radiation shielding community that increasing shielding thickness will decrease risk to crew health. A comprehensive modeling effort was undertaken to verify the preliminary modeling results using multiple Monte Carlo and deterministic space radiation transport codes. These results verified the preliminary findings of a minimum and helped drive the design of the experimental component of the project. In first-of-their-kind experiments performed at the NASA Space Radiation Laboratory, neutrons and light ions were measured between large thicknesses of aluminum shielding. Both an upstream and a downstream shield were incorporated into the experiment to represent the radiation environment inside a spacecraft. These measurements are used to validate the Monte Carlo codes and derive uncertainty distributions for exposure estimates behind thick shielding similar to that provided by spacecraft on a Mars mission. Preliminary results for all aspects of the project will be presented.

Norman, Ryan B.↗