Data sharing practices of medicines related apps and the mobile ecosystem: traffic, content, and network analysisBMJ 2019; 364 doi: https://doi.org/10.1136/bmj.l920 (Published 20 March 2019) Cite this as: BMJ 2019;364:l920
- Quinn Grundy, assistant professor and honorary senior lecturer12,
- Kellia Chiu, PhD candidate2,
- Fabian Held, senior research fellow2,
- Andrea Continella, postdoctoral fellow3,
- Lisa Bero, professor2,
- Ralph Holz, lecturer in networks and security4
- 1Faculty of Nursing, University of Toronto, Suite 130, 155 College St, Toronto, ON, Canada, M5T 1P8
- 2School of Pharmacy, Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- 3Department of Computer Science, University of California, Santa Barbara, CA, USA
- 4School of Computer Science, The University of Sydney, Sydney, NSW, Australia
- Correspondence to: Q Grundy @quinngrundy on Twitter) (or
- Accepted 25 February 2019
Objectives To investigate whether and how user data are shared by top rated medicines related mobile applications (apps) and to characterise privacy risks to app users, both clinicians and consumers.
Design Traffic, content, and network analysis.
Setting Top rated medicines related apps for the Android mobile platform available in the Medical store category of Google Play in the United Kingdom, United States, Canada, and Australia.
Participants 24 of 821 apps identified by an app store crawling program. Included apps pertained to medicines information, dispensing, administration, prescribing, or use, and were interactive.
Interventions Laboratory based traffic analysis of each app downloaded onto a smartphone, simulating real world use with four dummy scripts. The app’s baseline traffic related to 28 different types of user data was observed. To identify privacy leaks, one source of user data was modified and deviations in the resulting traffic observed.
Main outcome measures Identities and characterisation of entities directly receiving user data from sampled apps. Secondary content analysis of company websites and privacy policies identified data recipients’ main activities; network analysis characterised their data sharing relations.
Results 19/24 (79%) of sampled apps shared user data. 55 unique entities, owned by 46 parent companies, received or processed app user data, including developers and parent companies (first parties) and service providers (third parties). 18 (33%) provided infrastructure related services such as cloud services. 37 (67%) provided services related to the collection and analysis of user data, including analytics or advertising, suggesting heightened privacy risks. Network analysis revealed that first and third parties received a median of 3 (interquartile range 1-6, range 1-24) unique transmissions of user data. Third parties advertised the ability to share user data with 216 “fourth parties”; within this network (n=237), entities had access to a median of 3 (interquartile range 1-11, range 1-140) unique transmissions of user data. Several companies occupied central positions within the network with the ability to aggregate and re-identify user data.
Conclusions Sharing of user data is routine, yet far from transparent. Clinicians should be conscious of privacy risks in their own use of apps and, when recommending apps, explain the potential for loss of privacy as part of informed consent. Privacy regulation should emphasise the accountabilities of those who control and process user data. Developers should disclose all data sharing practices and allow users to choose precisely what data are shared and with whom.
Contributors: QG acquired funding, designed the study, supervised and participated in data collection and content analysis, and wrote the first draft of the manuscript. KC participated in data collection and content analysis and critically revised manuscript drafts. FH participated in designing the study, conducted the network analysis, and critically revised manuscript drafts. AC conducted the traffic analysis and critically revised manuscript drafts. LB participated in designing the study and commented on the draft. RH designed the study, supervised the traffic analysis, and critically revised manuscript drafts. QG attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. QG and RH act as guarantors.
Funding: This work was funded by a grant from the Sydney Policy Lab at The University of Sydney. QG was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research. The Sydney Policy Lab had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.
Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: this work was funded by the Sydney Policy Lab; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.
Ethical approval: Not required.
Data sharing: The full analysis is publicly available at: https://healthprivacy.info/.
Transparency: The lead author (QG) affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.
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