Objective: While patients often contribute data for research, they want researchers to protect their data. As part of a participatory design of privacy-enhancing software, this study explored patients' perceptions of privacy protection in research using their healthcare data.
Materials And Methods: We conducted 4 focus groups with 27 patients on privacy-enhancing software using the nominal group technique. We provided participants with an open source software prototype to demonstrate privacy-enhancing features and elicit privacy concerns. Participants generated ideas on benefits, risks, and needed additional information. Following a thematic analysis of the results, we deployed an online questionnaire to identify consensus across all 4 groups. Participants were asked to rank-order benefits and risks. Themes around "needed additional information" were rated by perceived importance on a 5-point Likert scale.
Results: Participants considered "allowance for minimum disclosure" and "comprehensive privacy protection that is not currently available" as the most important benefits when using the privacy-enhancing prototype software. The most concerning perceived risks were "additional checks needed beyond the software to ensure privacy protection" and the "potential of misuse by authorized users." Participants indicated a desire for additional information with 6 of the 11 themes receiving a median participant rating of "very necessary" and rated "information on the data custodian" as "essential."
Conclusions: Patients recognize not only the benefits of privacy-enhancing software, but also inherent risks. Patients desire information about how their data are used and protected. Effective patient engagement, communication, and transparency in research may improve patients' comfort levels, alleviate patients' concerns, and thus promote ethical research.
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http://dx.doi.org/10.1093/jamia/ocab073 | DOI Listing |
PeerJ Comput Sci
September 2024
College of Engineering, Karachi Institute of Economics and Technology, Karachi, Sindh, Pakistan.
Fintech is an industry that uses technology to enhance and automate financial services. Fintech firms use software, mobile apps, and digital technologies to provide financial services that are faster, more efficient, and more accessible than those provided by traditional banks and financial institutions. Fintech companies take care of processes such as lending, payment processing, personal finance, and insurance, among other financial services.
View Article and Find Full Text PDFNPJ Digit Med
October 2024
School of Software, Tsinghua University, Beijing, China.
The success of deep learning (DL) relies heavily on training data from which DL models encapsulate information. Consequently, the development and deployment of DL models expose data to potential privacy breaches, which are particularly critical in data-sensitive contexts like medicine. We propose a new technique named DiffGuard that generates realistic and diverse synthetic medical images with annotations, even indistinguishable for experts, to replace real data for DL model training, which cuts off their direct connection and enhances privacy safety.
View Article and Find Full Text PDFPLoS One
August 2024
Department of Computer Science, University of Brasilia, Federal District, Brasília, Brazil.
Domain Generation Algorithms (DGAs) are used by malware to generate pseudorandom domain names to establish communication between infected bots and command and control servers. While DGAs can be detected by machine learning (ML) models with great accuracy, offering DGA detection as a service raises privacy concerns when requiring network administrators to disclose their DNS traffic to the service provider. The main scientific contribution of this paper is to propose the first end-to-end framework for privacy-preserving classification as a service of domain names into DGA (malicious) or non-DGA (benign) domains.
View Article and Find Full Text PDFOrphanet J Rare Dis
July 2024
Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin, Berlin, Germany.
Background: Globally, researchers are working on projects aiming to enhance the availability of data for rare disease research. While data sharing remains critical, developing suitable methods is challenging due to the specific sensitivity and uniqueness of rare disease data. This creates a dilemma, as there is a lack of both methods and necessary data to create appropriate approaches initially.
View Article and Find Full Text PDFJAMIA Open
April 2024
Population Informatics Lab, Texas A&M University, College Station, TX 77843, United States.
Objective: In retrospective secondary data analysis studies, researchers often seek waiver of consent from institutional Review Boards (IRB) and minimize risk by utilizing complex software. Yet, little is known about the perspectives of IRB experts on these approaches. To facilitate effective communication about risk mitigation strategies using software, we conducted two studies with IRB experts to co-create appropriate language when describing a software to IRBs.
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