Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “security and privacy”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Unraveling the Noise: An Investigation Plan for Signal Interference in Hearing Aids

Bluetooth Low Energy (BLE) has revolutionized the performance of hearing aids with functionalities like seamless audio streaming and enhanced auditory functions. However, BLE operates within the highly congested 2.4 GHz frequency band, making it susceptible to signal interference that can degrade performance, reduce audio quality, and impact user experience. This paper documents interference patterns in BLE communication and introduces practical mitigation techniques aimed at improving the reliability of hearing aids. Attack vectors associated with Bluetooth enabled hearing aids include communication jamming, the interception of data between target devices, and GATT handle exploitation. Attackers could also use the vulnerabilities to block communications or intercept sensitive audio streams, posing significant security and privacy risks. These threats compromise two critical components of the CIA triad: (1) availability, by causing persistent connectivity issues, and (2) integrity, by enabling unauthorized data modifications. It is necessary to deal with these problems to ensure hearing aids work well and safely. This study investigates the impact of BLE signal interference on hearing aids, using tools such as HackRF [1], a Python tool to simulate interference scenarios, and Ubertooth [2] to sniff Bluetooth traffic between hearing aids and the device with the application. This paper investigates testing of BLE traffic in search of specific interference patterns that would impact the functionality of hearing aids, including jamming and flooding. This research focuses on developing robust mitigation techniques with the aim of securing BLE-enabled hearing aids against those vulnerabilities.

Baldwin, David [Savannah River National Laboratory↗
DOE OSTI2023

Fielding Freedom of Information Act (FOIA) requests at the National Security Research Center

For staff at the Lab’s National Security Research Center, fulfilling requests for information can sometimes be a bit otherworldly. Some of these requests are made under the Freedom of Information Act or FOIA, which are submitted to NNSA by the public and directed to LANL for records search and review. The topics of these requests range from technical reports to records on unidentified flying objects. “Fulfilling FOIA requests is an important part of what we do here,” said NSRC senior archivist Daniel Alcazar (WRS-NSRCMS). “It’s critical to our mission to ensure the public has access to information it has the right to see.” Congress passed the FOIA in 1967, giving any person the right to request access to federal agency records so they can better understand the U.S. Government’s operations and activities. Federal agencies are required to disclose information requested under the FOIA, unless it falls under one or more of nine exemptions protecting interests such as personal privacy, national security, and law enforcement. Often described as the law that keeps citizens in the know about the government, the FOIA is a vital part of U.S. democracy. The Laboratory’s FOIA program works with over a hundred requests for records each year. As the Lab’s classified research and technical library dedicated to information stewardship and education, the National Security Research Center (NSRC) regularly assists with these requests– as many as 45 per year. Public queries cover a spectrum of topics, but most common include requests for Manhattan Project era documents and memoranda, Los Alamos technical reports, Los Alamos-produced historical and scientific motion picture film and videos, as well as those seeking any documents or files pertaining to a former scientist, engineer or employee. While less common, Alcazar estimates that he receives a few FOIA requests a year related to unidentified flying objects (UFOs) and unexplained aerial phenomena (UAP).

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2016

Sleep Environment Recommendations for Future Spaceflight Vehicles

Current evidence demonstrates that astronauts experience sleep loss and circadian desynchronization during spaceflight. Ground-based evidence demonstrates that these conditions lead to reduced performance, increased risk of injuries and accidents, and short and long-term health consequences. Many of the factors contributing to these conditions relate to the habitability of the sleep environment. Noise, inadequate temperature and airflow, and inappropriate lighting and light pollution have each been associated with sleep loss and circadian misalignment during spaceflight operations and on Earth. As NASA prepares to send astronauts on long-duration, deep space missions, it is critical that the habitability of the sleep environment provide adequate mitigations for potential sleep disruptors. We conducted a comprehensive literature review summarizing optimal sleep hygiene parameters for lighting, temperature, airflow, humidity, comfort, intermittent and erratic sounds, and privacy and security in the sleep environment. We reviewed the design and use of sleep environments in a wide range of cohorts including among aquanauts, expeditioners, pilots, military personnel and ship operators. We also reviewed the specifications and sleep quality data arising from every NASA spaceflight mission, beginning with Gemini. Finally, we conducted structured interviews with individuals experienced sleeping in non-traditional spaces including oil rig workers, Navy personnel, astronauts, and expeditioners. We also interviewed the engineers responsible for the design of the sleeping quarters presently deployed on the International Space Station. We found that the optimal sleep environment is cool, dark, quiet, and is perceived as safe and private. There are wide individual differences in the preferred sleep environment; therefore modifiable sleeping compartments are necessary to ensure all crewmembers are able to select personalized configurations for optimal sleep. A sub-optimal sleep environment is tolerable for only a limited time, therefore individual sleeping quarters should be designed for long-duration missions. In a confined space, the sleep environment serves a dual purpose as a place to sleep, but also as a place for storing personal items and as a place for privacy during non-sleep times. This need for privacy during sleep and wake appears to be critically important to the psychological well-being of crewmembers on long-duration missions.

Flynn-Evans, Erin E.↗
DOE OSTI2024

Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

Abstract Cyber‐physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber‐physical systems requires improved understanding of the combined cyber‐physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber‐physical data from smart grid systems to analyse differences and similarities in behaviour during cyber‐, physical‐, and cyber‐physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber‐physical systems. Through this analysis, deeper insights can be shared with decision‐makers on what cyber and physical components are strongly or weakly linked, what cyber‐physical pathways are most traversed, and the criticality of certain cyber‐physical nodes or edges. This paper presents several types of clustering methods for cyber‐physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber‐physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber‐physical graph interdependency analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Extended convex hull-based distributed optimal energy flow of integrated electricity-gas systems

Integrated electricity and gas systems are constructed to facilitate the gas-fired generation, and the distributed operation of these integrated systems have received much attention due to the increased emphasis on data security and privacy between different agencies. This paper proposes an extended convex hull based method to address optimal energy flow problems for the integrated electricity and gas systems in a distributed manner. First, a multi-block electricity-gas system model is constructed by dividing the whole system into N blocks considering both physical and regional differences. This multi-block model is then convexified by replacing the nonconvex gas transmission equation with the extended convex hull-based constraints. The Jacobi-Proximal alternating direction method of multipliers algorithm is adopted to solve the convexified model and minimize its operation cost. Finally, the feasibility of the optimal solution for the convexified model is checked, and a sufficient condition is developed. If the sufficient condition is satisfied, the optimal solution for the original nonconvex problem can be recovered from that for the convexified problem. Simulation results demonstrate that the proposed method is tractable and effective in obtaining feasible optimal solutions for multi-block optimal energy flow problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Edge-cloud computing performance benchmarking for IoT based machinery vibration monitoring

Advances in low cost and reliable sensing, connectivity (Internet of Things), computational power, and advanced analytics, are leading to a new wave of innovation in machinery status sensing and condition monitoring. Significant research efforts are directed towards cloud computing architectures. However, given the latency, bandwidth, cost, security, and privacy concerns, further supported by the ever-increasing capabilities of edge computing devices, there is a need to consider both edge and cloud computing together to make informed decisions based upon context and performance. In this work, we present an edge-cloud performance evaluation for IoT based machinery vibration monitoring, to foster deployment for the contexts considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Remote inspection of adversary-controlled environments

Remotely monitoring the location and enduring presence of valuable items in adversary-controlled environments presents significant challenges. In this article, we demonstrate a monitoring approach that leverages the gigahertz radio-wave scattering and absorption of a room and its contents, including a set of mirrors with random orientations placed inside, to remotely verify the absence of any disturbance over time. Our technique extends to large physical systems the application of physical unclonable functions for integrity protection. Its main applications are scenarios where parties are mutually distrustful and have privacy and security constraints. Examples range from the verification of nuclear arms-control treaties to the securing of currency, artwork, or data centers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy

High-energy Two dimensional (2D) synchrotron x-ray diffractometry provides important insights into the atomistic structure and phase evolution of materials, yet traditional analysis methods remain complex, knowledge-intensive, and computationally demanding. Deep-learning models offer a powerful alternative for automating their analysis. Institutions that hold these datasets may be unwilling to share their data due to privacy and security policies, as well as the challenges associated with large-scale data transfer. As a result, models trained on local datasets often perform well only on their own data but exhibit bias and poor generalization across different instruments or facilities. To overcome these limitations, we explore federated learning (FL) for 2D synchrotron diffractograms, enabling collaborative model training without exchanging raw data. In this study, 2D synchrotron diffractograms of Ti–6Al–4V alloy collected from two independent facilities are used to train convolutional neural networks for predicting the β-phase volume fraction. Experimental results show that federated global models significantly outperform locally trained models in terms of generalization and achieve accuracy comparable to centralized trained models. These findings demonstrate the potential of FL to enable secure, cross-institutional collaboration and enhance the scalability of deep-learning-based materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evaluating Recursive Blind Forecast Against API and Baseline: A Puerto Rican Case Study on Solar Irradiance for Normal and Extreme Weather

This paper leverages ongoing work in a community microgrid in Adjuntas, Puerto Rico to forecast global horizontal irradiance (GHI) and compare performance in normal and extreme weather. Given a positive correlation of 0.98 between GHI and PV power, forecasting GHI can be an effective, indirect forecast of photovoltaic (PV) power, especially in microgrids where the end-users, owners, operators, or other stakeholders are reluctant to share data for training or validation due to privacy and security concerns. A recursive one-shot (termed as "blind") forecast is, hence, formulated, wherein a gradient-boosted regression tree (GBR) is built to forecast GHI for a 7-day horizon in normal weather, and a 2-day horizon in extreme weather. To demonstrate its resilience, the architecture is trained on normal and hurricane weather GHI from 2002-2022. It is generalized on February 9-16, 2023, and on the landfall of Hurricane Nicole (Nov 4-5, 2022), respectively. Forecasts from GBR are compared against that from a satellite-based API resource and three baselines: persistence, averaging, and exponential smoothing. Results show GBR and persistence outperform sophisticated API in both types of weather for this case study.

Sundararajan, Aditya↗
DOE OSTI2023

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The proliferation of the Machine-Learning-As-A-Service (MLaaS) market has brought to light a number of clients’ data privacy and security concerns. One promising solution is private inference (PI) techniques using cryptographic primitives. These techniques often come with high computation and communication overhead associated with the non-linear operator such as ReLU. Several approaches have been developed in reducing the number of ReLU operations, however, they either require a heuristic threshold selection or introduce significant accuracy drop. This work presents AutoReP, a gradient-based framework for non-linear operators reduction that aims to mitigate these concerns from a systematic perspective. AutoReP automates the process of discrete selection of ReLU and polynomial functions on neurons to accelerate PI applications. We also introduce distribution-aware polynomial approximation (DaPa) to accurately approximate ReLUs under given distribution, preserving model expressivity. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 × ReLU budget reduction.

Peng, Hongwu↗
DOE OSTI2022

Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

Gao, Peng↗
DOE OSTI2023

Quantum Computing for Biomedical Computational and Data Sciences: A Joint DOE-NIH Roundtable

The overlap of quantum computing and biomedical research, while less explored, presents significant near-term opportunities. The Department of Energy (DOE) and the National Institutes of Health (NIH) are interested in exploiting the DOE community’s capabilities and expertise in quantum computing to potentially advance biomedical research, targeting fundamental studies of biological and molecular structures, understanding of human health as well as mental and physical disorders and diseases, and deriving insights from clinical data. NIH’s approach to quantum computing is guided by its Strategic Plan for Data Science, emphasizing the importance of findable, accessible, interoperable, and reusable (FAIR) data assets, security and privacy of data, and efficient computing and storage. DOE’s Office of Science (SC), and more specifically the Advanced Scientific Computing Research (ASCR) program, supports quantum information science (QIS) research, contributing to a unique portfolio of quantum computing and communications expertise. This roundtable was assembled to consider the opportunities and challenges in the near-, medium-, and long-term at the intersection of quantum computing, data science, and biomedical research and how these could be addressed through inter-agency collaboration and multi-disciplinary partnerships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗
DOE OSTI2024

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1988

Telescience in the Space Station era

Telescience refers to the development of systems where participants involved in research in space can access their fellow scientists and the appropriate NASA services before flight, during flight, and after flight, preferably from their home institutions and through the same equipment. Telescience requires integration of available technologies to develop computer environments that maintain interoperability across different disciplines and different portions of the lifetimes of space experiments, called teledesign, teleoperations, and teleanalysis. Participants in the NASA Telescience Testbed Program are using a rigid prototyping approach to evaluate the necessary technologies and select the options and tradeoffs that best suit their accustomed modalities. The concept of transaction management is described, where the emphasis is placed on the effects of commands, whether event-generated onboard the spacecraft or sent up from the ground. Interoperability, security, and privacy issues are also discussed, and the Telescience Testbed Pilot Program is described.

Schmerling, E. R.↗
NASA NTRS1993

The Database Query Support Processor (QSP)

The number and diversity of databases available to users continues to increase dramatically. Currently, the trend is towards decentralized, client server architectures that (on the surface) are less expensive to acquire, operate, and maintain than information architectures based on centralized, monolithic mainframes. The database query support processor (QSP) effort evaluates the performance of a network level, heterogeneous database access capability. Air Force Material Command's Rome Laboratory has developed an approach, based on ANSI standard X3.138 - 1988, 'The Information Resource Dictionary System (IRDS)' to seamless access to heterogeneous databases based on extensions to data dictionary technology. To successfully query a decentralized information system, users must know what data are available from which source, or have the knowledge and system privileges necessary to find out this information. Privacy and security considerations prohibit free and open access to every information system in every network. Even in completely open systems, time required to locate relevant data (in systems of any appreciable size) would be better spent analyzing the data, assuming the original question was not forgotten. Extensions to data dictionary technology have the potential to more fully automate the search and retrieval for relevant data in a decentralized environment. Substantial amounts of time and money could be saved by not having to teach users what data resides in which systems and how to access each of those systems. Information describing data and how to get it could be removed from the application and placed in a dedicated repository where it belongs. The result simplified applications that are less brittle and less expensive to build and maintain. Software technology providing the required functionality is off the shelf. The key difficulty is in defining the metadata required to support the process. The database query support processor effort will provide quantitative data on the amount of effort required to implement an extended data dictionary at the network level, add new systems, adapt to changing user needs, and provide sound estimates on operations and maintenance costs and savings.

Source record↗
NASA NTRS2011

Secure Intra-Body Wireless Communications (SIWiC) System Project

SIWiC System is a project to investigate, design and implement future wireless networks of implantable sensors in the body. This futuristic project is designed to make use of the emerging and yet-to-emerge technologies, including ultra-wide band (UWB) for wireless communications, smart implantable sensors, ultra low power networking protocols, security and privacy for bandwidth and power deficient devices and quantum computing. Progress in each of these fronts is hindered by the needs of breakthrough. But, as we will see in this paper, these major challenges are being met or will be met in near future. SIWiC system is a network of in-situ wireless devices that are implanted to coordinate sensed data inside the body, such as symptoms monitoring collected internally, or biometric data collected of an outside object from within the intra-body network. One node has the capability of communicating outside the body to send data or alarm to a relevant authority, e.g., a remote physician.

Ahmad, Aftab↗
NASA NTRS2016

Sleep Environment Recommendations for Future Spaceflight Vehicles

We conducted a comprehensive literature review summarizing optimal sleep hygiene parameters for lighting, temperature, airflow, humidity, comfort, intermittent and erratic sounds, and privacy and security in the sleep environment. We reviewed the design and use of sleep environments in a wide range of cohorts including among aquanauts, expeditioners, pilots, military personnel and ship operators. We also reviewed the specifications and sleep quality data arising from every NASA spaceflight mission, beginning with Gemini. Finally, we conducted structured interviews with individuals experienced in sleeping in non-traditional spaces including oilrig workers, Navy personnel, astronauts, and expeditioners. We also interviewed the engineers responsible for the design of the sleeping quarters presently deployed on the International Space Station. We found that the optimal sleep environment is cool, dark, quiet, and is perceived as safe and private. There are wide individual differences in the preferred sleep environment; therefore modifiable sleeping compartments are necessary to ensure all crewmembers are able to select personalized configurations for optimal sleep. It is possible to utilize lessons learned from prior spaceflight missions and from other industries in order to guide the design of an optimal sleep space suitable for long-duration spaceflight.

spaceflight↗