An Information Privacy Risk Index for mHealth Apps

Brüggemann, Thomas; Hansen, Joel; Dehling, Tobias; Sunyaev, Ali

doi:10.1007/978-3-319-44760-5_12

Cited by 14 publications

(17 citation statements)

References 20 publications

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“…According to the type of source, 19 out of the 24 articles (79%) were published in journals [ 6 , 14 , 15 , 19 , 20 , 22 , 24 - 33 , 37 - 39 ], whereas 4 (17%) were published in conference proceedings [ 23 , 34 - 36 ] and 1 (4%) was a book chapter [ 21 ]. The publication fields were quite heterogeneous, with 12 out of 24 articles (50%) pertaining to the area of medical informatics [ 21 - 24 , 26 - 28 , 32 , 33 , 36 , 37 , 39 ], 5 (21%) to medicine [ 14 , 15 , 25 , 29 , 38 ], 4 (17%) to information technology [ 19 , 30 , 31 , 34 ], 2 (8%) to security and privacy [ 20 , 35 ], and 1 (4%) to a multidisciplinary source [ 6 ].…”

Section: Resultsmentioning

confidence: 99%

“…None of the selected articles was published between 2009 and 2013. Out of the 24 papers, 4 (17%) were published in 2014 [ 23 , 28 , 31 , 34 ], 4 (17%) in 2015 [ 19 , 24 , 30 , 38 ], 3 (13%) in 2016 [ 20 , 21 , 36 ], 2 (8%) in 2017 [ 25 , 26 ], 7 (29%) in 2018 [ 6 , 14 , 22 , 27 , 32 , 35 , 37 ], and 4 (17%) in the first half of 2019 [ 15 , 29 , 33 , 39 ].…”

Section: Resultsmentioning

confidence: 99%

“…For instance, in Knorr et al [ 30 ], both diabetes and blood pressure apps were analyzed. Fitness apps, including self-tracking and physical activity apps, were the most analyzed, appearing in 6 articles (25%) [ 20 - 22 , 24 , 32 , 36 ]. Mental health apps, including apps for depression monitoring, were assessed in 5 articles (21%) [ 15 , 26 , 33 , 37 , 39 ], and diabetes-related apps appeared in 4 articles (17%) [ 6 , 29 , 30 , 37 ].…”

Section: Resultsmentioning

confidence: 99%

“…Of the 24 articles assessed, 12 (50%) [ 14 , 15 , 20 - 24 , 27 , 31 , 33 , 37 , 39 ] evaluated only privacy; 8 (33%) evaluated security features, together with privacy [ 6 , 19 , 30 , 32 , 34 - 36 , 38 ]; and 4 (17%) conducted a multidimensional assessment [ 25 , 26 , 28 , 29 ], with privacy being only part of the evaluation.…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Privacy Assessment in Mobile Health Apps: Scoping Review

Benjumea

Ropero

Rivera-Romero

et al. 2020

JMIR Mhealth Uhealth

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Background Privacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments. Objective This scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed. Methods We followed the methodology defined by Arksey and O’Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy. Results The database searches retrieved a total of 710 citations—24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps. Conclusions The privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Privacy Assessment in Mobile Health Apps: Scoping Review

Benjumea

Ropero

Rivera-Romero

et al. 2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Finally, the third category of studies are those that provide evaluation techniques targeted at the later stages in the app lifecycle (18.2% or 8/44): evaluations targeting clinicians to support them in recommending mHealth to their patients or direct evaluation for adoption by app users. Evaluation at this stage was mostly focused on the information available from app providers and app functionalities, for instance, to assess app quality [74], intervention relevance [76], [77], or privacy risks specifically [52].…”

Section: Compliance Data Protection Regulations Compliancementioning

confidence: 99%

Security and Privacy of mHealth Applications: A Scoping Review

2020

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While digital health or mHealth applications (apps) have become accessible resources for the support of personal health, the privacy and security of users' data have been the subject of concern and controversy. As large numbers of mHealth apps are created and are increasingly widely used by people with various health conditions, it is crucial to have clear and valid methods for evaluating the data practices within them. Recent regulatory initiatives such as the European Union's General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) have had the effect of raising awareness and establishing a minimal set of expectations. However, they do not in themselves address the issue of the development of systems which meet privacy and security requirements. There is a growing body of research on evaluation techniques and frameworks to support the assessment of the privacy and security of health apps, and guidelines to support their design. However, it can be challenging to navigate this space and choose appropriate techniques for a given context. Addressing this issue, this paper examines the recent literature on security and privacy of m-Health applications, using a scoping review methodology. It analyses data security and privacy evaluation techniques and frameworks that have been proposed for mHealth applications, as well as relevant research-based design recommendations. This work consolidates recent research on the topic to support researchers, app designers, end users, and healthcare professionals in designing, evaluating, recommending and adopting mHealth applications.

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Human-Centered Design for Data-Sparse Tailored Privacy Information Provision

Goram

Dehling

Morsbach³

et al. 2023

Human Factors in Privacy Research

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One way to reduce privacy risks for consumers when using the internet is to inform them better about the privacy practices they will encounter. Tailored privacy information provision could outperform the current practice where information system providers do not much more than posting unwieldy privacy notices. Paradoxically, this would require additional collection of data about consumers’ privacy preferences—which constitute themselves sensitive information so that sharing them may expose consumers to additional privacy risks. This chapter presents insights on how this paradoxical interplay can be outmaneuvered. We discuss different approaches for privacy preference elicitation, the data required, and how to best protect the sensitive data inevitably to be shared with technical privacy-preserving mechanisms. The key takeaway of this chapter is that we should put more thought into what we are building and using our systems for to allow for privacy through human-centered design instead of static, predefined solutions which do not meet consumer needs.

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An Information Privacy Risk Index for mHealth Apps

Cited by 14 publications

References 20 publications

Privacy Assessment in Mobile Health Apps: Scoping Review

Privacy Assessment in Mobile Health Apps: Scoping Review

Security and Privacy of mHealth Applications: A Scoping Review

Human-Centered Design for Data-Sparse Tailored Privacy Information Provision

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