Privacy in mobile technology for personal healthcare

Avancha, Sasikanth; Baxi, Amit; Kotz, David

doi:10.1145/2379776.2379779

Cited by 187 publications

(130 citation statements)

References 99 publications

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“…We additionally checked if the apps in our test set in its current or earlier version are listed in the National Vulnerability Database http://www.nvd.org (NVD) and found no entries. AVG classified com.stevenmz.BloodPressureDiary as malware and MMSL.BGGlucoDiary as "Potentially Unwanted Program" without giving a detailed explanation 5 . Avast and McAfee did not find any malware, privacy or security issue.…”

Section: Resultsmentioning

confidence: 99%

“…In the US, the Health Insurance Portability and Accountability Act (HIPAA) sets legal security and privacy standards for electronically transmitted health information; in the EU, there are diverse country-specific laws that need to be respected [5]. Devices that collect measurements which are used for monitoring, treatment, or diagnosis of health conditions are medical devices and these are regulated by the Food and Drug Administration in the US.…”

Section: Privacy Threats and Relevant Regulationmentioning

confidence: 99%

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On the Privacy, Security and Safety of Blood Pressure and Diabetes Apps

Knorr

Aspinall

Wolters

2015

ICT Systems Security and Privacy Protection

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Abstract. Mobile health (mHealth) apps are an ideal tool for monitoring and tracking long-term health conditions. In this paper, we examine whether mHealth apps succeed in ensuring the privacy, security, and safety of the health data entrusted to them. We investigate 154 apps from Android app stores using both automatic code and metadata analysis and a manual analysis of functionality and data leakage. Our study focuses on hypertension and diabetes, two common health conditions that require careful tracking of personal health data. We find that many apps do not provide privacy policies or safe communications, are implemented in an insecure fashion, fail basic input validation tests and often have overall low code quality which suggests additional security and safety risks. We conclude with recommendations for App Stores, App developers, and end users.

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Section: Resultsmentioning

confidence: 99%

Section: Privacy Threats and Relevant Regulationmentioning

confidence: 99%

On the Privacy, Security and Safety of Blood Pressure and Diabetes Apps

Knorr

Aspinall

Wolters

2015

ICT Systems Security and Privacy Protection

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“…In the current study, privacy in relation to m-healthcare context is referred to as the individual"s right to control the acquisition, uses, or disclosures of his or her identifiable health data (Avancha et al, 2012). However, security is defined as the physical, technological, or administrative safeguards or tools used to protect identifiable health data from unwarranted access or disclosure (Avancha et al, 2012).…”

Section: External Variables: Social Influence Trust Security/privacmentioning

confidence: 99%

Mobile Healthcare Adoption among Patients in a Developing Country Environment: Exploring the Influence of Age and Gender Differences

Faqih¹,

Jaradat²

2015

IBR

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This research was motivated by two considerations: (1) Mobile-based technologies have the potential to improve the delivery process of healthcare services, and (2) limited research has been implemented worldwide to focus on patients" perceptions towards the adoption of mobile healthcare, particularly in developing country environment. This research proposes an extended TAM model as a research framework to better understand the adoption process of mobile healthcare among patients. To serve the objectives of this study, a paper questionnaire was employed to collect data. Previously validated set of measurement items were used to develop the survey instrument. The proposed research model was validated using the PLS-SEM approach (WarpPLS 4.0) with a sample of 366 respondents. The results of the current study have provided adequate statistical support for the extended TAM model. With the exception of cost, all external variables incorporated in this model (including perceived ease of use, perceived usefulness, social influence, trust, and security/privacy) are found influential in shaping the patients" perceptions towards the adoption of mobile-healthcare technology. In addition, the current study demonstrates that demographic variables of age and gender have considerable moderating influence on the adoption of mobile technologies in healthcare systems in Jordan. The current research model can serve as a blueprint for future expansion of research in this vital field of study. Theoretical contributions, practical implications and future research directions of the study are also addressed.

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“…Hence, a plethora of research surveys has been recently released [37]- [42], focusing mainly on the M2M area network.…”

Section: ) Challengesmentioning

confidence: 99%

Machine-to-machine (M2M) communications for e-health applications

Kartsakli

Lalos

Antonopoulos

et al. 2015

Machine-to-Machine (M2M) Communications

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The emerging technology of Machine-to-Machine (M2M) communications is bringing a paradigm shift in healthcare delivery. A broad range of e-Health applications can be conceived, with considerable benefits for both patients and healthcare providers. Many technological challenges have to be met, however, to ensure the widespread adoption of e-Health solutions in the future. In this context, we aim to provide a comprehensive overview on M2M systems for e-Health applications from a wireless communication perspective. We provide an overview of the candidate wireless technologies that are suitable for different parts of the M2M system architecture and then show how these technologies are seamlessly integrated to provide an end-to-end e-Health solution. In particular, we discuss end-toend solution designs and testbed implementations, we present the key security and privacy challenges associated with the sensitive nature of medical data and we summarize the most recent research projects dedicated to e-Health applications. Index TermsMachine-to-Machine (M2M), e-Health, WBAN, M2M Area Network, M2M Communications Network, IEEE 802.15.6, end-to-end communications

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Privacy in mobile technology for personal healthcare

Cited by 187 publications

References 99 publications

On the Privacy, Security and Safety of Blood Pressure and Diabetes Apps

On the Privacy, Security and Safety of Blood Pressure and Diabetes Apps

Mobile Healthcare Adoption among Patients in a Developing Country Environment: Exploring the Influence of Age and Gender Differences

Machine-to-machine (M2M) communications for e-health applications

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