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Design and implementation of a credible blockchain-based e-health records platformXu, Lingyu January 2020 (has links)
>Magister Scientiae - MSc / With the development of information and network technologies, Electronic Health
Records (EHRs) management system has gained wide spread application in
managing medical records. One of the major challenges of EHRs is the independent
nature of medical institutions. This non-collaborative nature puts a significant
barrier between patients, doctors, medical researchers and medical data. Moreover,
unlike the unique and strong anti-tampering nature of traditional paper-based
records, electronic health records stored in centralization database are vulnerable to
risks from network attacks, forgery and tampering. In view of the data sharing
difficulties and information security problems commonly found in existing EHRs,
this dissertation designs and develops a credible Blockchain-based electronic
health records (CB-EHRs) management system.
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Design and Implementation of a Credible Blockchain-based E-health Records PlatformXu, Lingyu January 2020 (has links)
Masters of Science / With the development of information and network technologies, Electronic Health Records (EHRs) management system has gained wide spread application in managing medical records. One of the major challenges of EHRs is the independent nature of medical institutions. This non-collaborative nature puts a significant barrier between patients, doctors, medical researchers and medical data. Moreover, unlike the unique and strong anti-tampering nature of traditional paper-based records, electronic health records stored in centralization database are vulnerable to risks from network attacks, forgery and tampering. In view of the data sharing difficulties and information security problems commonly found in existing EHRs, this dissertation designs and develops a credible Blockchain-based electronic health records (CB-EHRs) management system. To improve security, the proposed system combines digital signature (using MD5 and RSA) with Role-Based Access
Control (RBAC). The advantages of these are strong anti-tampering, high stability, high security, low cost, and easy implementation. To test the efficacy of the system, implementation was done using Java web programming technology. Tests were carried out to determine the efficiency of the Delegated Byzantine Fault Tolerance (dBFT) consensus algorithm, functionality of the RBAC mechanism and the various system modules. Results obtained show that the system can manage and share EHRs safely and effectively. The expectation of the author is that the output of this research would foster the development and adaptation of EHRs management system.
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A Qualitative Study of Adolescents’ Understanding of Biobanks and Their Attitudes Towards Participation, Re-contact and Data SharingMurad, Andrea M. 30 June 2015 (has links)
No description available.
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Hierarchical Bayesian Dataset SelectionZhou, Xiaona 05 1900 (has links)
Despite the profound impact of deep learning across various domains, supervised model training critically depends on access to large, high-quality datasets, which are often challenging to identify. To address this, we introduce <b>H</b>ierarchical <b>B</b>ayesian <b>D</b>ataset <b>S</b>election (<b>HBDS</b>), the first dataset selection algorithm that utilizes hierarchical Bayesian modeling, designed for collaborative data-sharing ecosystems. The proposed method efficiently decomposes the contributions of dataset groups and individual datasets to local model performance using Bayesian updates with small data samples. Our experiments on two benchmark datasets demonstrate that HBDS not only offers a computationally lightweight solution but also enhances interpretability compared to existing data selection methods, by revealing deep insights into dataset interrelationships through learned posterior distributions. HBDS outperforms traditional non-hierarchical methods by correctly identifying all relevant datasets, achieving optimal accuracy with fewer computational steps, even when initial model accuracy is low. Specifically, HBDS surpasses its non-hierarchical counterpart by 1.8% on DIGIT-FIVE and 0.7% on DOMAINNET, on average. In settings with limited resources, HBDS achieves a 6.9% higher accuracy than its non-hierarchical counterpart. These results confirm HBDS's effectiveness in identifying datasets that improve the accuracy and efficiency of deep learning models when collaborative data utilization is essential. / Master of Science / Deep learning technologies have revolutionized many domains and applications, from voice recognition in smartphones to automated recommendations on streaming services. However, the success of these technologies heavily relies on having access to large and high-quality datasets. In many cases, selecting the right datasets can be a daunting challenge. To tackle this, we have developed a new method that can quickly figure out which datasets or groups of datasets contribute most to improving the performance of a model with only a small amount of data needed. Our tests prove that this method is not only effective and light on computation but also helps us understand better how different datasets relate to each other.
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Motivating Subjects: Data Sharing in Cancer ResearchTucker, Jennifer 30 September 2009 (has links)
This dissertation explores motivation in decision-making and action in science and technology, through the lens of a case study: scientific data sharing in cancer research. The research begins with the premise that motivation and emotion are key elements of what it means to be human, and consequently, are important variables in how individuals make decisions and take action. At the same time, institutional controls and social messaging send a variety of signals intended to motivate specific actions and behaviors. Understanding the interplay between personal motives and social influences may point to strategies that better align individual and social perceptions and discourse.
To explore these dynamics, this research centers on a large-scale cancer research program led by the National Institutes of Health's National Cancer Institute. The goal of the program is to encourage interoperability and data sharing between diverse and highly autonomous cancer centers across the U.S. Housed in an organization focused on biomedical informatics, the program has a technologically-focused mission; the goal is to facilitate institutional data sharing to connect the cancer research enterprise.
This focus contrasts with the more relationship-based point-to-point data sharing currently reported by researchers as the norm. Researchers are motivated to share data with others under specific conditions: when there is a foundation of trust with the person or community being shared with; when the perceived reward of sharing is well-defined and of value to the person sharing; and when there is perceived to be a lower risk or cost than the benefit received. Without these conditions, there are often determined to be insufficient incentives and rewards for sharing.
Data sharing is both a personal decision and a social level problem. Data is both subjective and personal; it is often an extension of researcher's identity, and serves as a measure of his or her value and capability. In the search for standards and interoperable data sets, institutional and technologically-mediated forms of data sharing are perceived to ignore the subjective and local knowledge embodied in the data being shared. To explore these dimensions, this study considers the technology, economics, legal elements, and personal sides of data sharing, and applies two conceptual frameworks to evaluate alternatives for action. / Ph. D.
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Deidentification of Face Videos in Naturalistic Driving ScenariosThapa, Surendrabikram 05 September 2023 (has links)
The sharing of data has become integral to advancing scientific research, but it introduces challenges related to safeguarding personally identifiable information (PII). This thesis addresses the specific problem of sharing drivers' face videos for transportation research while ensuring privacy protection. To tackle this issue, we leverage recent advancements in generative adversarial networks (GANs) and demonstrate their effectiveness in deidentifying individuals by swapping their faces with those of others. Extensive experimentation is conducted using a large-scale dataset from ORNL, enabling the quantification of errors associated with head movements, mouth movements, eye movements, and other human factors cues. Additionally, qualitative analysis using metrics such as PERCLOS (Percentage of Eye Closure) and human evaluators provide valuable insights into the quality and fidelity of the deidentified videos. To enhance privacy preservation, we propose the utilization of synthetic faces as substitutes for real faces. Moreover, we introduce practical guidelines, including the establishment of thresholds and spot checking, to incorporate human-in-the-loop validation, thereby improving the accuracy and reliability of the deidentification process. In addition to this, this thesis also presents mitigation strategies to effectively handle reidentification risks. By considering the potential exploitation of soft biometric identifiers or non-biometric cues, we highlight the importance of implementing comprehensive measures such as robust data user licenses and privacy protection protocols. / Master of Science / With the increasing availability of large-scale datasets in transportation engineering, ensuring the privacy and confidentiality of sensitive information has become a paramount concern. One specific area of concern is the protection of drivers' facial data captured by the National Driving Simulator (NDS) during research studies. The potential risks associated with the misuse or unauthorized access to such data necessitate the development of robust deidentification techniques. In this thesis, we propose a GAN-based framework for the deidentification of drivers' face videos while preserving important facial attribute information. The effectiveness of the proposed framework is evaluated through comprehensive experiments, considering various metrics related to human factors. The results demonstrate the capability of the framework to successfully deidentify face videos, enabling the safe sharing and analysis of valuable transportation research data. This research contributes to the field of transportation engineering by addressing the critical need for privacy protection while promoting data sharing and advancing human factors research.
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Applying User-Centered Design to Build Trust and Enable Cross-Agency Data Sharing to Better Understand the Prevalence and Context of Human Trafficking in Alberta, CanadaMuyres, Natalie Suzanne 05 1900 (has links)
This thesis describes a research study to understand the current state of human trafficking data collection practices and reporting in the province of Alberta, Canada, and gather end-user design considerations for cyberinfrastructure that will enable data sharing between multiple and diverse stakeholders. The research also examines the barriers to change and the needs of stakeholders to improve the collective understanding of human trafficking in the province. Virtual semi-structured interviews were conducted with nineteen organizations including law enforcement and community serving non-profit agencies. While the research resulted in design considerations for a cyberinfrastructure prototype, findings revealed the complexity of the human trafficking ecosystem in Alberta and suggestions for how the community of stakeholders can shift towards a sustained collaborative data sharing culture. This study responds to the need to collect actionable data from multiple sources to reflect the prevalence and context of human trafficking more accurately.
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DSAP: Data Sharing Agreement Privacy Ontology / Privacy Ontology for Health Data Sharing in ResearchLi, Mingyuan January 2018 (has links)
Medical researchers utilize data sharing agreements (DSA) to communicate privacy policies that govern the treatment of data in their collaboration. Expression of privacy policies in DSAs have been achieved through the use of natural and policy languages. However, ambiguity in natural language and rigidness in policy languages make them unsuitable for use in collaborative medical research. Our goal is to develop an unambiguous and flexible form of expression of privacy policies for collaborative medical research. In this thesis, we developed a DSA Privacy Ontology to express privacy policies in medical research. Our ontology was designed with hierarchy structure, lightweight in expressivity, closed world assumption in interpretation, and the reuse of other ontologies. The design allows our ontology to be flexible and extensible. Being flexible allows our ontology to express different types of privacy policies. Being extensible allows our ontology to be mapped to other linkable ontologies without the need to change our existing ontology. We demonstrate that our ontology is capable of supporting the DSA in a collaborative research data sharing scenario through providing the appropriate vocabulary and structure to log privacy events in a linked data based audit log. Furthermore, through querying the audit log, we can answer privacy competency questions relevant to medical researchers. / Thesis / Master of Science (MSc)
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Selling Telemetry Data Over the Internet Using SETKalibjian, Jeffrey R. 10 1900 (has links)
International Telemetering Conference Proceedings / October 26-29, 1998 / Town & Country Resort Hotel and Convention Center, San Diego, California / Over the past two years the design and implementation of secure Internet based data sharing tools which could enable geographically remote contractor teams to access flight and test telemetry data securely over the Internet were presented [1] [2]. Key technologies facilitating these capabilities were the Hypertext Transfer (HTTP) protocol , the Secure Sockets Layer (SSL) protocol, and the Secure Multipurpose Internet Mail Extension (S/MIME) specification . This year we discuss utilizing the Secure Electronic Transaction (SET) specification in tandem with HTTP, SSL, and S/MIME to deploy a system for securely selling telemetry data over the Internet.
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Customer data in the European fashion industry : Investigation of students’ willingnessto share customer data in the fashion e-commerceHöler, Lisa Marie, Meyer zu Hörste, Christin January 2016 (has links)
The present thesis aims to investigate students’ willingness to share customer data in the fashion e-commerce. Special attention is given to the way trust, benefits and law regulations are discussed in this context. An inductive approach was applied utilizing focus group discussions. The goal of the empirical study was to gain insights in the way students argue about customer data and which feelings are involved. The study highlights findings in the key areas trust, benefits and law regulations. A shift of customer trust from brands to recommendations could be observed. In terms of benefits, focus group participants tend to choose value exchange over traditional rewards. Findings regarding law regulations suggest that the General Data Protection Regulation of the European Union can provide control of customer data but no security. Furthermore, consumer behavior plays an important role when it comes to data sharing attitudes. The experiences and observations of the participants may not be applicable to other studies. Furthermore, the study findings are bounded to the European Union. The findings suggest that trust, benefits and law regulations can influence students’ willingness to share customer data in the fashion e-commerce. The study is unique of its kind as it investigates the willingness to share customer data with the focus on students and fashion e-commerce. Hence, this research paper fills a gap in scientific literature and is valuable for businesses operating in the fashion e-commerce.
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