SEARCH · Engineering Papers
Results for “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.
Encrypted Decentralized Multi-Agent Optimization for Privacy Preservation in Cyber-Physical Systems
Not provided.
Channel State Information-Free Location-Privacy Enhancement: Fake Path Injection
Not provided.
CEDS Differential Privacy (CEDSDP) v0.1
A Python package that provides differentially private queries optimized for energy systems' data. It may be used to publish queries such as clustering, averaging, metadata inference, etc. that are useful for a variety of grid-related analytics, including cyberattack detection.
Controlling Information and Privacy Loss in Machine Learning Models with Coarseness
Explore the source record for details and available documents.
A security analysis of version 2 of the Network Time Protocol (NTP): A report to the privacy and security research group
The Network Time Protocol is being used throughout the Internet to provide an accurate time service. The security requirements are examined of such a service, version 2 of the NTP protocol is analyzed to determine how well it meets these requirements, and improvements are suggested where appropriate.
Protecting Astronaut Medical Privacy: Review of Presentations and Publications for Attributability
Retrospective research and medical data collected on astronauts can be a valuable resource for researchers. This data can be requested from two separate NASA Archives. The Lifetime Surveillance of Astronaut Health (LSAH) holds astronaut medical data, and the Life Sciences Data Archive (LSDA) holds research data. One condition of use of astronaut research and medical data is the requirement that all abstracts, publications and presentations using this data must be reviewed for attributability. All final versions of abstracts, presentations, posters, and manuscripts must be reviewed by LSDA/LSAH prior to submission to a conference, journal, or other entities outside the Principal Investigator (PI) laboratory [including the NASA Export Control Document Availability Authorization (DAA) system]. If material undergoes multiple revisions (e.g., journal editor comments), the new versions must also be reviewed by LSDA/LSAH prior to re-submission to the journal. The purpose of this review is to ensure that no personally identifiable information (PII) is included in materials that are presented in a public venue or posted to the public domain. The procedures for submitting materials for review will be outlined. The process that LSAH/LSDA follows for assessing attributability will be presented. Characteristics and parameter combinations that often prompt attributability concerns will be identified. A published case report for a National Football League (NFL) player will be used to demonstrate how, in a population of public interest, a combination of information can result in inadvertent release of private or sensitive information.
Watching without Seeing: A Tool to Surveil Astronaut Health Outcomes While Maintaining Astronaut Medical Privacy
Explore the source record for details and available documents.
Drone delivery and the value of customer privacy: A discrete choice experiment with U.S. consumers
Explore the source record for details and available documents.
A Local Electricity Market for Transactive Energy with Privacy
Presentation in PESGM Panel on "Multi-Agent Systems for Transactive Energy: Theory, Platforms, and Demonstrations"
Quantifying Vulnerability of Privacy Attacks toward the MT-CNN models for Information Extraction from Cancer Pathology Reports
Explore the source record for details and available documents.
Driving Road Safety Forward: Video Data Privacy Task at MediaEval 2021
Explore the source record for details and available documents.
Use My Data, But Don't Make Me Share It: Hybrid Secure MultiParty Computation for Privacy-Preserving Machine Learning.
Abstract not provided.
DP-HPC: Bringing Differential Privacy to HPC Systems Log Sharing and Analysis
Explore the source record for details and available documents.