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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Data Confidentiality Challenges in Big Data Applications

In this paper, we address the problem of data confidentiality in big data analytics. In many fields, much useful patterns can be extracted by applying machine learning techniques to big data. However, data confidentiality must be protected. In many scenarios, data confidentiality could well be a prerequisite for data to be shared. We present a scheme to provide provable secure data confidentiality and discuss various techniques to optimize performance of such a system.

data confidentiality↗
DOE OSTI2016

Practical State Machine Replication with Confidentiality

We study how to enable arbitrary randomized algorithms in Byzantine fault-tolerant (BFT) settings. We formalize a randomized BFT protocol and provide a simple and efficient construction that can be built on any existing BFT protocols while adding practically no overhead. We go one step further to revisit a confidential BFT protocol (Yin et al., SOSP '03). We show that their scheme is potentially susceptible to safety and confidentiality attacks. We then present a new protocol that is secure in the stronger model we formalize, by extending the idea of a randomized BFT protocol. Our protocol uses only efficient symmetric cryptography, while Yin et al.'s uses costly threshold signatures. We implemented and evaluated our protocols on microbenchmarks and real-world use cases. We show that our randomized BFT protocol is as efficient as conventional BFT protocols, and our confidential BFT protocol is two to three orders of magnitude faster than Yin et al.'s, which is less secure than ours.

Duan, Sisi↗
DOE OSTI2018

A method for processing the Confidential Carload Waybill Sample for railroad freight analysis

Freight transportation research is often constrained by the availability of appropriate data. Within the context of freight rail research, use of traditional freight datasets present some critical challenges. Aggregating freight data to geographic areas diminishes the ability to perform nuanced analysis. More challenging, the complexity of railroad operations limits the usability of publicly available freight flow data which do not account for which rail company is economically and legally responsible for the movement of the goods or for the contractual arrangements between infrastructure owners and carriers. Finally, detailed data are usually privately owned and not readily-accessible for analysis outside the individual rail companies. As part of their regulatory authority, the Surface Transportation Board has developed the Confidential Carload Waybill Sample, a stratified sample representing 1% to 3% of all rail traffic in the United States for a particular year. While the sample is rich with data, it is limited in spatial information as well as in its implementation in geospatial analysis. This paper introduces a methodology for geospatially enabling the Confidential Carload Waybill Sample which can then be used to generate rail routes based on a topological representation of the U.S. railroad network. As a result, this methodology is applied using two case studies, a freight diversion analysis and transporting hazardous materials.

42 ENGINEERING↗
DOE OSTI2023

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Guide to Data Access and Utility Customer Confidentiality

This report explains the difference between whole-building energy usage data requests and other types of data requests utilities may receive. It outlines utility best practices for providing energy consumption information to building owners while respecting the confidentiality of utility customers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Remediation of Occupied Commercial Property Subject to Widespread Radium-226 Contamination - Confidential Client in the South-West of England - 12570

AMEC was contacted by a company that managed commercial office space in 2010. High Rn- 222 measurements had been observed throughout the facility and the landlord had been advised to commission a radiological survey of the site. The site had been purchased by the client in the 1990's. Initial desk studies found that the building had operated for around 50 years as a compass factory. Non-intrusive investigation identified widespread Ra-226 contamination. Ra-226 was found in the fabric of the building, in attic spaces, buried under floor boards and underlying car parks. Intrusive investigation was undertaken to estimate volume(s) of waste, waste categories, activity concentrations and the total inventory of radioactive materials on site. This work identified the presence of 180 GBq of Ra-226 on site. A programme of work is currently underway to remediate the site tackling areas posing the greatest risk to site occupants as a priority. We have worked closely with Regulators, our client, and tenants, to decontaminate the fabric of the building whilst areas of the building remain occupied. The radiological risk, from irradiation, ingestion and inhalation (of Ra-226 and Rn- 222) has been assessed before, during and after intervention to minimise the risks to site occupants. Tenants were moved from areas of unacceptable radiological risk to areas unaffected by the presence of radioactive materials. Rn-222 mitigation measures were installed during the remedial operations to minimise the hazard from Rn-222 that was liberated as a result of decontamination activities. Decontamination techniques were required to be sympathetic to the building as the ageing structure was in danger of collapse during several phases of work. The first phase of remediation is now complete and the decontaminated building is being returned for use as office space. The radiological risks have been significantly reduced and, in areas where decontamination was not possible (e.g. due to concerns over the structural integrity of the building), mitigation measures have been installed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

User Access to Scientific Facilities via 5G: A Cyber Security Thought Experiment

5G is more than an over-the-air radio technology upgrade. It is a strategy to extend Mobile Network Operator service offerings beyond traditional voice, instant messaging and Internet access. 5G Mobile Network Operators will offer new telecommunication services that include enhanced guarantees of confidentiality, integrity and availability. How could such services change the way Science collaborations connect scientists to supercomputers and other scientific facilities? Current scientific collaborations implicitly trust cloud service providers to securely store and process data. The perceived risks of outsourcing Science data security are counterbalanced by assurances that cloud providers operate at a scale that allows them to implement security measures impractical for Science collaborations (e.g. continuous system administrator behavioral monitoring and strict individual separation of duties). If that is true for a cloud service provider like Amazon Web Services (2018 revenue: $25.7 billion), could it also be true for Mobile Network Operators like Verizon Wireless (2018 revenue: $91.7 billion) or AT&T Mobility (2018 revenue: $71.3 billion)? DOE Leadership Class supercomputer facility users currently access them from the public Internet via Secure Shell. The sponsors and operators of the supercomputer facilities have determined that the public Internet path between the Scientist’s Device and the Login Node does not natively provide enough confidentiality or integrity to protect those communications. Therefore, the facilities achieve additional confidentiality and integrity by requiring Secure Shell encryption across those untrusted network paths. Using 5G Network Slice technology, a Mobile Network Operator may offer communication services between supercomputer users and facilities that natively provide confidentiality and integrity guarantees. Sponsors and operators of supercomputer facilities may determine that these guarantees provide enough confidentiality and integrity to protect those communications. If so, a 5G Network Slice could replace an SSH session running over the public Internet. Finally, this use case could be extended to other Office of Science user facility access requirements. Consider microscopy instruments at (e.g.) the Center for Nanoscale Materials or the Environmental Molecular Sciences Laboratory. The embedded systems controlling such instruments may not always support encrypted network access technologies like SSH. 5G Network Slices may offer an alternative to current VPN or SSH tunneling techniques, with additional benefits like guaranteed minimum bandwidth.

5G↗
DOE OSTI2015

Cyber Contingency Analysis version 1.x

Contingency analysis based approach for quantifying and examining the resiliency of a cyber system in respect to confidentiality, integrity and availability. A graph representing an organization's cyber system and related resources is used for the availability contingency analysis. The mission critical paths associated with an organization are used to determine the consequences of a potential contingency. A node (or combination of nodes) are removed from the graph to analyze a particular contingency. The value of all mission critical paths that are disrupted by that contingency are used to quantify its severity. A total severity score can be calculated based on the complete list of all these contingencies. A simple n1 analysis can be done in which only one node is removed at a time for the analysis. We can also compute nk analysis, where k is the number of nodes to simultaneously remove for analysis. A contingency risk score can also be computed, which takes the probability of the contingencies into account. In addition to availability, we can also quantify confidentiality and integrity scores for the system. These treat user accounts as potential contingencies. The amount (and type) of files that an account can read to is used to compute the confidentiality score. The amount (and type) of files that an account can write to is used to compute the integrity score. As with availability analysis, we can use this information to compute total severity scores in regards to confidentiality and integrity. We can also take probability into account to compute associated risk scores.

Mackey, PatrickS.↗
DOE OSTI2020

Cybersecurity impact of the growth of data in sports

As the sporting industry continues to increase dependence on new and often massive data sets, they also risk exposing themselves, as well as the leagues, teams, venues, players, and fans, to risks posed by not protecting the confidentiality and integrity of this data. The industry, especially true for professional sports, demands an ever increasing supply of new forms of data to achieve their goals of enticing new fans; enhancing experience; expanding the fan experience from venues and television to the internet and mobile devices; measuring and evaluating the performance and safety of players; and increasing revenue. New methods have emerged to collect data about a variety of aspects of sports ranging from players to fans to teams to venues. Data collected during sporting events is combined with historical data to also help teams analyze and predict performance. To synthesize these predictive analytical and historical memories, sports teams and organizations mix a variety of computer technologies. System integration tends to focus on ensuring the availability of the data and reliability of the technologies but might introduce risks to the confidentiality (privacy) and integrity of the data. Recognizing the potential risks requires review and evaluation. Much of the data is collected for publicly providing information to the fans, so threats to confidentiality pose fewer risks. The greatest risk is violating the privacy of players, such a private performance test, or using technology to gain a competitive advantage.

Jenkins, Stephanie↗
DOE OSTI2017

Security and Cloud Outsourcing Framework for Economic Dispatch

The computational complexity and problem sizes of power grid applications have increased significantly with the advent of renewable resources and smart grid technologies. The current paradigm of solving these issues consist of inhouse high performance computing infrastructures, which have drawbacks of high capital expenditures, maintenance, and limited scalability. Cloud computing is an ideal alternative due to its powerful computational capacity, rapid scalability, and high cost-effectiveness. A major challenge, however, remains in that the highly confidential grid data is susceptible for potential cyberattacks when outsourced to the cloud. In this work, a security and cloud outsourcing framework is developed for the Economic Dispatch (ED) linear programming application. As a result, the security framework transforms the ED linear program into a confidentiality-preserving linear program, that masks both the data and problem structure, thus enabling secure outsourcing to the cloud. Results show that for large grid test cases the performance gain and costs outperforms the in-house infrastructure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Los Alamos National Laboratory Ombuds 2012 Report

Established in 1996, the Ombuds Office (“Ombuds”) helps individuals explore concerns and weigh options using informal conflict resolution, coaching and enhancement of communication skills. The Ombuds assists visitors through individual consultations, facilitative processes and skills-building presentations. Ombuds is the resource of choice for employees seeking a confidential, informal venue in which to explore their concerns. The mission of the Ombuds Office is to enhance the overall productivity of the Laboratory workforce by offering assistance in resolving work-related problems informally, confidentially and impartially. It is a unique resource to help visitors identify their options, resolve disputes, and engage in general problem solving. Ombuds enables employees to stay focused and resolve conflict, thereby contributing to a safe, secure, and productive workplace.

99 GENERAL AND MISCELLANEOUS↗