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

Martindale, Nathan; Stewart, Scott; McGirl, Natalie; Adams, Mark; Westphal, Greg; Garner, James

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

Full Record
Other Related Research

Abstract

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.

Authors:

Martindale, Nathan ^[1];

^[1]; McGirl, Natalie ^[1];

^[1]; Westphal, Greg ^[1];

^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:: Fri Oct 01 00:00:00 EDT 2021

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1827049

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Nuclear Materials Management

Additional Journal Information:: Journal Volume: 49; Journal Issue: 2; Related Information: https://www.ingentaconnect.com/content/inmm/jnmm/2021/00000049/00000002/art00003; Journal ID: ISSN 0893-6188

Publisher:: Institute of Nuclear Materials Management

Country of Publication:: United States

Language:: English

Subject:: 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; Design Information Verification; Information Barrier; Machine Learning; Privacy-Preserving Techniques

Citation Formats


                    Martindale, Nathan, Stewart, Scott, McGirl, Natalie, Adams, Mark, Westphal, Greg, and Garner, James. Enabling Computation on Sensitive Data in International Safeguards with Privacy-Preserving Encryption Techniques.  United States: N. p., 2021. 
Web.

Copy to clipboard


                    Martindale, Nathan, Stewart, Scott, McGirl, Natalie, Adams, Mark, Westphal, Greg, & Garner, James. Enabling Computation on Sensitive Data in International Safeguards with Privacy-Preserving Encryption Techniques.  United States.

Copy to clipboard


                    Martindale, Nathan, Stewart, Scott, McGirl, Natalie, Adams, Mark, Westphal, Greg, and Garner, James. Fri .  
"Enabling Computation on Sensitive Data in International Safeguards with Privacy-Preserving Encryption Techniques".  United States.  https://www.osti.gov/servlets/purl/1827049.

Copy to clipboard


                    
@article{osti_1827049,

  title        = {Enabling Computation on Sensitive Data in International Safeguards with Privacy-Preserving Encryption Techniques},

  author       = {Martindale, Nathan and Stewart, Scott and McGirl, Natalie and Adams, Mark and Westphal, Greg and Garner, James},

  abstractNote = {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.},

  doi          = {},

  journal      = {Journal of Nuclear Materials Management},

  number       = 2,

  volume       = 49,

  place        = {United States},

  year         = {Fri Oct 01 00:00:00 EDT 2021},

  month        = {Fri Oct 01 00:00:00 EDT 2021}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

The DOI is not currently available

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Considerations for using Privacy Preserving Machine Learning Techniques for Safeguards

Technical Report Martindale, Nathan ; Stewart, Scott L. ; Adams, Mark ; ...

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), securemore »« less
https://doi.org/10.2172/1737477

Full Text Available
Mitigate: An Adaptive Network Data Anonymization Tool Using Condensation-Based Differential Privacy

Technical Report Karabatis, George ; Chen, Zhiyuan ; Aleroud, Ahmed

Modern network devices collect a large amount of data that can be analyzed to identify bottlenecks, anomalies, cyber-attacks, etc. Therefore, there is often a need to analyze such collections of network data quite often by an external expert or by the research community. However, these collections of data contain sensitive, proprietary information. In order for the network data to be shared, it must first be anonymized. The overall objective of this project is to develop an innovative privacy management tool to anonymize network data and achieve sufficient privacy, acceptable data utility, and efficient data analysis at the same time. Nomore »« less
https://doi.org/10.2172/1854575

Full Text Available
Homomorphic Encryption for Machine Learning and Artificial Intelligence Applications

Technical Report Arnold, David ; Saniie, Jafar ; Heifetz, Alexander

Third-party and expert analysis is a cost-effective solution for solving specialized problems or processing large datasets related to reactor structural health monitoring and nondestructive evaluation. However, when handling proprietary information, third-party and expert analysts pose a privacy risk. To address this challenge, Homomorphic Encryption (HE) permits arithmetic operations on encrypted data without exposing the underlying data. Implementations of Machine Learning (ML) and Artificial Intelligence (AI) algorithms using HE greatly enhances the capabilities of third-party analysts while maintaining a low security risk. This paper details current success in applying Principal Component Analysis (PCA) and Fully Connected Neural Networks (NN) using themore »« less
https://doi.org/10.2172/1886256

Full Text Available
Audit Report on "Protection of the Department of Energy's Unclassified Sensitive Electronic Information"

Technical Report

The Department of Energy and its contractors store and process massive quantities of sensitive information to accomplish national security, energy, science, and environmental missions. Sensitive unclassified data, such as personally identifiable information (PII), official use only, and unclassified controlled nuclear information require special handling and protection to prevent misuse of the information for inappropriate purposes. Industry experts have reported that more than 203 million personal privacy records have been lost or stolen over the past three years, including information maintained by corporations, educational institutions, and Federal agencies. The loss of personal and other sensitive information can result in substantial financialmore »« less
https://doi.org/10.2172/963952

Full Text Available
Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology

Journal Article Yoon, Hong-Jun ; Stanley, Christopher B. ; Christian, J. Blair ; ... - Cancer Biomarkers

With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the data and subject identities have also become an important issue and essential research topic. Without intentional safeguards, machine learning models may find patterns and features to improve task performance that are associated with private personal information. The privacy vulnerability of deep learning models for information extraction from medical textural contents needs to be quantified since the models are exposed to private health information and personally identifiable information. The objective of the study is to quantify the privacy vulnerability of the deepmore »« less
https://doi.org/10.3233/cbm-210306

Full Text Available

Similar Records