Journal of Medical Internet Research

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What Is Being Used and Who Is Using It: Barriers to the Adoption of Smartphone Patient Experience Surveys

Previous research has categorized users based on the intensity of their perceptions of privacy risk. Westin [32] separates technology users into 1 of the 3 risk groups based on their willingness to share personal information on the Web: (1) privacy fundamentalists (high privacy orientation and supports regulatory controls), (2) privacy pragmatists (weigh benefits to self or society and bases trust on context), and (3) privacy unconcerned (willing to share information and reject privacy concerns).

Denise Ng, Josephine McMurray, James Wallace, Plinio Morita

JMIR Form Res 2019;3(1):e9922

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Investigating the Extent to Which Patients Should Control Access to Patient Records for Research: A Deliberative Process Using Citizens’ Juries

Recruitment questionnaires collected data to enable selection against a priori criteria based on demographics and views on privacy (Table 1). The demographics provided a broadly representative sample of resident adults in England based on the 2011 census with respect to gender, age range, ethnicity, and educational attainment [19]. Potential participants were asked to complete an Ipsos MORI survey question [20] that involved balancing privacy against information sharing for public benefit (Textbox 2).

Mary P Tully, Kyle Bozentko, Sarah Clement, Amanda Hunn, Lamiece Hassan, Ruth Norris, Malcolm Oswald, Niels Peek

J Med Internet Res 2018;20(3):e112

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Contextual Anonymization for Secondary Use of Big Data in Biomedical Research: Proposal for an Anonymization Matrix

Anonymization is a means of preventing a breach of confidentiality and preserving privacy. Anonymized data are not protected under data protection law. Confidentiality and privacy are related concepts: confidentiality is a duty owed, often by a professional, to an individual in particular circumstances; privacy is a right that a person enjoys.

John Mark Michael Rumbold, Barbara Pierscionek

JMIR Med Inform 2018;6(4):e47

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Decentralizing Health Care: History and Opportunities of Web3

In patient data management, platforms such as Patientory (Patientory Inc) are empowering patients to store and manage their health data on blockchain, offering unprecedented control and privacy. Furthermore, health care systems are adopting smart contracts to streamline insurance processes, reducing administrative burdens and increasing transparency [5].

Aditya Narayan, Kydo Weng, Nirav Shah

JMIR Form Res 2024;8:e52740

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Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project

However, stringent privacy regulations protecting patient confidentiality often hamper the prompt availability of these data sets for research and educational usage [10-14]. Gaining access to clinical and health care data sets is a critical aspect of health data science education. This exposure provides trainees with invaluable practical experience, offering profound insights into the complexities of real-world health care scenarios [15].

Nicholas I-Hsien Kuo, Oscar Perez-Concha, Mark Hanly, Emmanuel Mnatzaganian, Brandon Hao, Marcus Di Sipio, Guolin Yu, Jash Vanjara, Ivy Cerelia Valerie, Juliana de Oliveira Costa, Timothy Churches, Sanja Lujic, Jo Hegarty, Louisa Jorm, Sebastiano Barbieri

JMIR Med Educ 2024;10:e51388

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Patients’ Perspectives on the Data Confidentiality, Privacy, and Security of mHealth Apps: Systematic Review

The influence of these interventions on patient perception on the security, confidentiality, and privacy of data collected via m Health apps is presented in the thematic analysis. The thematic analysis generated four broad themes from the findings of the studies: (1) privacy, confidentiality, and security; (2) awareness of privacy, security, and confidentiality; (3) facilitators and enablers; and (4) associated factors.

Nasser Alhammad, Mohannad Alajlani, Alaa Abd-alrazaq, Gregory Epiphaniou, Theodoros Arvanitis

J Med Internet Res 2024;26:e50715

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Checking Questionable Entry of Personally Identifiable Information Encrypted by One-Way Hash Transformation

Generally PII includes an ID (eg, patient ID, social security number, or national ID), name, birth date, birth place, address, postcode, and so on [3]; however, sharing PII may lead to disclosing privacy of an individual. Therefore, when medical data is shared, privacy protection is a very important task of biomedical research [4,5], especially when PII is a concern [6]. Patient data must be protected before they are transferred [7,8].

Xianlai Chen, Yang C Fann, Matthew McAuliffe, David Vismer, Rong Yang

JMIR Med Inform 2017;5(1):e2

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Strengthening Cybersecurity for Patient Data Protection in Europe

Technologies like blockchain [25-27] and a community solid server, which furnishes individuals with their personal data storage spaces [28], have been effective in addressing concerns related to patient privacy breaches.

Robin van Kessel, Madeleine Haig, Elias Mossialos

J Med Internet Res 2023;25:e48824

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Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review

However, concerns regarding privacy protection—a fundamental human right [25]—are rising amid increasing numbers of misconducts and violations [26,27]. In response to both data demands and privacy challenges, 2 groups of arguments can be made in favor of transitioning traditional approaches toward a new data science paradigm.

José Miguel Diniz, Henrique Vasconcelos, Júlio Souza, Rita Rb-Silva, Carolina Ameijeiras-Rodriguez, Alberto Freitas

JMIR Res Protoc 2023;12:e45823

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Acceptance and Privacy Perceptions Toward Video-based Active and Assisted Living Technologies: Scoping Review

The barrier with the greatest weight within the tradeoff process is privacy concerns. These concerns arise from the feelings of surveillance, fear of personal data access and misuse, intrusiveness, or the invasion of personal space [6,10,12,21]. With a focus on privacy, Lorenzen-Huber and colleagues [12] developed a framework that includes factors influencing the perception of privacy when adopting home-based ubiquitous technologies.

Tamara Mujirishvili, Caterina Maidhof, Francisco Florez-Revuelta, Martina Ziefle, Miguel Richart-Martinez, Julio Cabrero-García

J Med Internet Res 2023;25:e45297

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