3,190 research outputs found
Secure and Trustable Electronic Medical Records Sharing using Blockchain
Electronic medical records (EMRs) are critical, highly sensitive private
information in healthcare, and need to be frequently shared among peers.
Blockchain provides a shared, immutable and transparent history of all the
transactions to build applications with trust, accountability and transparency.
This provides a unique opportunity to develop a secure and trustable EMR data
management and sharing system using blockchain. In this paper, we present our
perspectives on blockchain based healthcare data management, in particular, for
EMR data sharing between healthcare providers and for research studies. We
propose a framework on managing and sharing EMR data for cancer patient care.
In collaboration with Stony Brook University Hospital, we implemented our
framework in a prototype that ensures privacy, security, availability, and
fine-grained access control over EMR data. The proposed work can significantly
reduce the turnaround time for EMR sharing, improve decision making for medical
care, and reduce the overall costComment: AMIA 2017 Annual Symposium Proceeding
Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective
Rapid advances in human genomics are enabling researchers to gain a better
understanding of the role of the genome in our health and well-being,
stimulating hope for more effective and cost efficient healthcare. However,
this also prompts a number of security and privacy concerns stemming from the
distinctive characteristics of genomic data. To address them, a new research
community has emerged and produced a large number of publications and
initiatives.
In this paper, we rely on a structured methodology to contextualize and
provide a critical analysis of the current knowledge on privacy-enhancing
technologies used for testing, storing, and sharing genomic data, using a
representative sample of the work published in the past decade. We identify and
discuss limitations, technical challenges, and issues faced by the community,
focusing in particular on those that are inherently tied to the nature of the
problem and are harder for the community alone to address. Finally, we report
on the importance and difficulty of the identified challenges based on an
online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies
(PoPETs), Vol. 2019, Issue
Enhancing Privacy and Authorization Control Scalability in the Grid through Ontologies
© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The use of data Grids for sharing relevant data has
proven to be successful in many research disciplines. However, the
use of these environments when personal data are involved (such
as in health) is reduced due to its lack of trust. There are many approaches
that provide encrypted storages and key shares to prevent
the access from unauthorized users. However, these approaches
are additional layers that should be managed along with the authorization
policies. We present in this paper a privacy-enhancing
technique that uses encryption and relates to the structure of the
data and their organizations, providing a natural way to propagate
authorization and also a framework that fits with many use cases.
The paper describes the architecture and processes, and also shows
results obtained in a medical imaging platform.Manuscript received November 19, 2007; revised July 27, 2008. First published August 4,2008; cur-rent version published January 4,2009. This work was supported in part by the Spanish Ministry of Education and Science to develop the project "ngGrid-New Generation Components for the Efficient Exploitation of eScience Infrastructures," under Grant TIN2006-12860 and in part by the Structural Funds of the European Regional Development Fund (ERDF).Blanquer Espert, I.; Hernández GarcÃa, V.; Segrelles Quilis, JD.; Torres Serrano, E. (2009). Enhancing Privacy and Authorization Control Scalability in the Grid through Ontologies. IEEE Transactions on Information Technology in Biomedicine. 13(1):16-24. https://doi.org/10.1109/TITB.2008.2003369S162413
Privacy in the Genomic Era
Genome sequencing technology has advanced at a rapid pace and it is now
possible to generate highly-detailed genotypes inexpensively. The collection
and analysis of such data has the potential to support various applications,
including personalized medical services. While the benefits of the genomics
revolution are trumpeted by the biomedical community, the increased
availability of such data has major implications for personal privacy; notably
because the genome has certain essential features, which include (but are not
limited to) (i) an association with traits and certain diseases, (ii)
identification capability (e.g., forensics), and (iii) revelation of family
relationships. Moreover, direct-to-consumer DNA testing increases the
likelihood that genome data will be made available in less regulated
environments, such as the Internet and for-profit companies. The problem of
genome data privacy thus resides at the crossroads of computer science,
medicine, and public policy. While the computer scientists have addressed data
privacy for various data types, there has been less attention dedicated to
genomic data. Thus, the goal of this paper is to provide a systematization of
knowledge for the computer science community. In doing so, we address some of
the (sometimes erroneous) beliefs of this field and we report on a survey we
conducted about genome data privacy with biomedical specialists. Then, after
characterizing the genome privacy problem, we review the state-of-the-art
regarding privacy attacks on genomic data and strategies for mitigating such
attacks, as well as contextualizing these attacks from the perspective of
medicine and public policy. This paper concludes with an enumeration of the
challenges for genome data privacy and presents a framework to systematize the
analysis of threats and the design of countermeasures as the field moves
forward
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Dwarna : a blockchain solution for dynamic consent in biobanking
Dynamic consent aims to empower research partners and facilitate active participation in the research process. Used within
the context of biobanking, it gives individuals access to information and control to determine how and where their
biospecimens and data should be used. We present Dwarna—a web portal for ‘dynamic consent’ that acts as a hub
connecting the different stakeholders of the Malta Biobank: biobank managers, researchers, research partners, and the
general public. The portal stores research partners’ consent in a blockchain to create an immutable audit trail of research
partners’ consent changes. Dwarna’s structure also presents a solution to the European Union’s General Data Protection
Regulation’s right to erasure—a right that is seemingly incompatible with the blockchain model. Dwarna’s transparent
structure increases trustworthiness in the biobanking process by giving research partners more control over which research
studies they participate in, by facilitating the withdrawal of consent and by making it possible to request that the biospecimen
and associated data are destroyed.peer-reviewe
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