9 research outputs found
Securing Medical Devices and Protecting Patient Privacy in the Technological Age of Healthcare
The healthcare industry has been adopting technology at an astonishing rate. This technology has served to increase the efficiency and decrease the cost of healthcare around the country. While technological adoption has undoubtedly improved the quality of healthcare, it also has brought new security and privacy challenges to the industry that healthcare IT manufacturers are not necessarily fully prepared to address.
This dissertation explores some of these challenges in detail and proposes solutions that will make medical devices more secure and medical data more private. Compared to other industries the medical space has some unique challenges that add significant constraints on possible solutions to problems. For example, medical devices must operate reliably even in the face of attack. Similarly, due to the need to access patient records in an emergency, strict enforcement of access controls cannot be used to prevent unauthorized access to patient data. Throughout this work we will explore particular problems in depth and introduce novel technologies to address them.
Each chapter in this dissertation explores some aspect of security or privacy in the medical space. We present tools to automatically audit accesses in electronic medical record systems in order to proactively detect privacy violations; to automatically fingerprint network-facing protocols in order to non-invasively determine if particular devices are vulnerable to known attacks; and to authenticate healthcare providers to medical devices without a need for a password in a way that protects against all known attacks present in radio-based authentication technologies. We also present an extension to the widely-used beacon protocol in order to add security in the face of active attackers; and we demonstrate an overhead-free solution to protect embedded medical devices against previously unpreventable attacks that evade existing control- flow integrity enforcement techniques by leveraging insecure built-in features in order to maliciously exploit configuration vulnerabilities in devices
Understanding Quantum Technologies 2022
Understanding Quantum Technologies 2022 is a creative-commons ebook that
provides a unique 360 degrees overview of quantum technologies from science and
technology to geopolitical and societal issues. It covers quantum physics
history, quantum physics 101, gate-based quantum computing, quantum computing
engineering (including quantum error corrections and quantum computing
energetics), quantum computing hardware (all qubit types, including quantum
annealing and quantum simulation paradigms, history, science, research,
implementation and vendors), quantum enabling technologies (cryogenics, control
electronics, photonics, components fabs, raw materials), quantum computing
algorithms, software development tools and use cases, unconventional computing
(potential alternatives to quantum and classical computing), quantum
telecommunications and cryptography, quantum sensing, quantum technologies
around the world, quantum technologies societal impact and even quantum fake
sciences. The main audience are computer science engineers, developers and IT
specialists as well as quantum scientists and students who want to acquire a
global view of how quantum technologies work, and particularly quantum
computing. This version is an extensive update to the 2021 edition published in
October 2021.Comment: 1132 pages, 920 figures, Letter forma
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
Active provenance for data intensive research
The role of provenance information in data-intensive research is a significant topic of
discussion among technical experts and scientists. Typical use cases addressing traceability,
versioning and reproducibility of the research findings are extended with more
interactive scenarios in support, for instance, of computational steering and results
management. In this thesis we investigate the impact that lineage records can have on
the early phases of the analysis, for instance performed through near-real-time systems
and Virtual Research Environments (VREs) tailored to the requirements of a specific
community. By positioning provenance at the centre of the computational research
cycle, we highlight the importance of having mechanisms at the data-scientists’ side
that, by integrating with the abstractions offered by the processing technologies, such
as scientific workflows and data-intensive tools, facilitate the experts’ contribution to
the lineage at runtime. Ultimately, by encouraging tuning and use of provenance for
rapid feedback, the thesis aims at improving the synergy between different user groups
to increase productivity and understanding of their processes.
We present a model of provenance, called S-PROV, that uses and further extends
PROV and ProvONE. The relationships and properties characterising the workflow’s
abstractions and their concrete executions are re-elaborated to include aspects related
to delegation, distribution and steering of stateful streaming operators. The model is
supported by the Active framework for tuneable and actionable lineage ensuring the
user’s engagement by fostering rapid exploitation. Here, concepts such as provenance
types, configuration and explicit state management allow users to capture complex
provenance scenarios and activate selective controls based on domain and user-defined
metadata. We outline how the traces are recorded in a new comprehensive system,
called S-ProvFlow, enabling different classes of consumers to explore the provenance
data with services and tools for monitoring, in-depth validation and comprehensive
visual-analytics. The work of this thesis will be discussed in the context of an existing
computational framework and the experience matured in implementing provenance-aware
tools for seismology and climate VREs. It will continue to evolve through
newly funded projects, thereby providing generic and user-centred solutions for data-intensive
research
Summary of Research 1994
The views expressed in this report are those of the authors and do not reflect the
official policy or position of the Department of Defense or the U.S. Government.This report contains 359 summaries of research projects which were carried out
under funding of the Naval Postgraduate School Research Program. A list of recent
publications is also included which consists of conference presentations and
publications, books, contributions to books, published journal papers, and
technical reports. The research was conducted in the areas of Aeronautics and
Astronautics, Computer Science, Electrical and Computer Engineering, Mathematics,
Mechanical Engineering, Meteorology, National Security Affairs, Oceanography,
Operations Research, Physics, and Systems Management. This also includes research
by the Command, Control and Communications (C3) Academic Group, Electronic Warfare
Academic Group, Space Systems Academic Group, and the Undersea Warfare Academic
Group
Personality Identification from Social Media Using Deep Learning: A Review
Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed