444 research outputs found
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
REISCH: incorporating lightweight and reliable algorithms into healthcare applications of WSNs
Healthcare institutions require advanced technology to collect patients' data accurately and continuously. The tradition technologies still suffer from two problems: performance and security efficiency. The existing research has serious drawbacks when using public-key mechanisms such as digital signature algorithms. In this paper, we propose Reliable and Efficient Integrity Scheme for Data Collection in HWSN (REISCH) to alleviate these problems by using secure and lightweight signature algorithms. The results of the performance analysis indicate that our scheme provides high efficiency in data integration between sensors and server (saves more than 24% of alive sensors compared to traditional algorithms). Additionally, we use Automated Validation of Internet Security Protocols and Applications (AVISPA) to validate the security procedures in our scheme. Security analysis results confirm that REISCH is safe against some well-known attacks
Information Management in Product Development Workflows – A Novel Approach on the basis of Pseudonymization of Product Information
AbstractInformation stored in the documentation of a product constitutes in many aspects the intellectual property (IP) of an enterprise. This valuable knowledge, built over years of extensive research and development deserves special attention and protection. Especially the context of distributed product development activities and increased collaborations with external partners puts companies at a growing risk that unauthorized individuals obtain access to this prized capital. In this paper, we present a novel concept for managing and sharing sensitive information in product development processes. Product information is separated and subsequently pseudonymized into independent blocks of data fragments which can be reassembled to specific information levels depending on the requirements of the organization. Thus, a user can be given access to that level of information specifically required to complete the task. The product information itself is only available as unordered data fragments and no longer interpretable even in case of data theft. By doing so, a comprehensive protection against internal and external abuse of sensitive product information can be realized which can easily be combined with existing concepts in the field of information protection
An Investigation of the Security Designs of a Structured Query Language (Sql) Database and its Middleware Application and their Secure Implementation Within Thinclient Environments
The Information Portability and Accountability Act (HIPAA) and The SarbanesOxley (SOX) regulations greatly influenced the health care industry regarding the means of securing financial and private data within information and technology. With the introduction of thinclient technologies into medical information systems (IS), data security and regulation compliancy becomes more problematic due to the exposure to the World Wide Web (WWW) and malicious activity. This author explores the best practices of the medical industry and information technology industry for securing electronic data within the thinclient environment at the three levels of architecture: the SQL database, its middleware application, and Web interface. Designing security within the SQL database is not good enough as breaches can occur through unintended consequences during data access within the middleware application design and data exchange design over computer networks. For example, a hospital\u27s medical records, which are routinely exchanged over computer networks, are subject to the audit control an encryption requirements mandated for data security. (Department of, 2008). While there is an overlapping of security techniques within each of the three layers of architectural security design, the use of 18 methodologies greatly enhances the ability to protect electronic information. Due to the variety of IS used within a medical facility, security conscientiousness, consistency of security design, excellent communication between designers, developers and system engineers, and the use of standardized security techniques within each of the three layers of architecture are required
From Raw Data to FAIR Data: The FAIRification Workflow for Health Research
BackgroundFAIR (findability, accessibility, interoperability, and reusability) guidingprinciples seek the reuse of data and other digital research input, output, and objects(algorithms, tools, and workflows that led to that data) making themfindable, accessible,interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governedinitiative-defined a seven-step FAIRificationprocessfocusingondata,butalsoindicatingtherequired work for metadata. This FAIRification process aims at addressing the translation ofraw datasets into FAIR datasets in a general way, without considering specific requirementsand challenges that may arise when dealing with some particular types of data.This work was performed in the scope of FAIR4Healthproject. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovationprogramme under grant agreement number 824666
Privacy-Preserving Data Integration for Health
The digital transformation of health processes has resulted in the collection of vast amounts of health-related data that presents significant potential to support medical research projects and improve the healthcare system. Many of these possibilities arise as a consequence of integrating data from different sources to create an accurate and unified representation of the underlying data and enable detailed data analysis that is not possible through any individual source. Achieving this vision requires the collection and processing of sensitive health-related data about individuals, thus privacy and confidentiality implications have to be considered. In this paper, I describe my doctoral research topic: the design and development of a novel Privacy-Preserving Data Integration (PPDI) framework which aims to effectively address the challenges and opportunities of integrating Big Health Data (BHD) while ensuring compliance with the General Data Protection Regulation (GDPR). The paper describes the planned methodology for implementing the PPDI process through the usage of data pseudonymization techniques and Privacy-Preserving Record Linkage (PPRL) methods and provides an overview of the new framework, which is based on the re-implementation of MOMIS towards a microservices architecture with added PPDI functionalities
Analyzing Partitioned FAIR Health Data Responsibly
It is widely anticipated that the use of health-related big data will enable
further understanding and improvements in human health and wellbeing. Our
current project, funded through the Dutch National Research Agenda, aims to
explore the relationship between the development of diabetes and socio-economic
factors such as lifestyle and health care utilization. The analysis involves
combining data from the Maastricht Study (DMS), a prospective clinical study,
and data collected by Statistics Netherlands (CBS) as part of its routine
operations. However, a wide array of social, legal, technical, and scientific
issues hinder the analysis. In this paper, we describe these challenges and our
progress towards addressing them.Comment: 6 pages, 1 figure, preliminary result, project repor
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