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Title: A STUDY OF PERSONAL INFORMATION LEAKS IN MOBILE MEDICAL, HEALTH, AND FITNESS APPS
There has been a proliferation of mobile apps in the Medical, as well as Health&Fitness categories. These apps have a wide audience, from medical providers, to patients, to end users who want to track their fitness goals. The low barrier to entry on mobile app stores raises questions about the diligence and competence of the developers who publish these apps, especially regarding the practices they use for user data collection, processing, and storage. To help understand the nature of data that is collected, and how it is processed, as well as where it is sent, we developed a tool named PIT (Personal Information Tracker) and made it available as open source. We used PIT to perform a multi-faceted study on 2832 Android apps: 2211 Medical apps and 621 Health&Fitness apps. We first define Personal Information (PI) as 17 different groups of sensitive information, e.g., user’s identity, address and financial information, medical history or anthropometric data. PIT first extracts the elements in the app’s User Interface (UI) where this information is collected. The collected information could be processed by the app’s own code or third-party code; our approach disambiguates between the two. Next, PIT tracks, via static analysis, where the information is “leaked”, i.e., it escapes the scope of the app, either locally on the phone or remotely via the network. Then, we conduct a link analysis that examines the URLs an app connects with, to understand the origin and destination of data that apps collect and process. We found that most apps leak 1–5 PI items (email, credit card, phone number, address, name, being the most frequent). Leak destinations include the network (25%), local databases (37%), logs (23%), and files or I/O (15%). While Medical apps have more leaks overall, as they collect data on medical history, surprisingly, Health&Fitness apps also collect, and leak, medical data. We also found that leaks that are due to third-party code (e.g., code for ads, analytics, or user engagement) are much more numerous (2x–12x) than leaks due to app’s own code. Finally, our link analysis shows that most apps access 20–80 URLs (typically third-party URLs and Cloud APIs) though some apps could access more than 1,000 URLs.  more » « less
Award ID(s):
2106710
PAR ID:
10623615
Author(s) / Creator(s):
; ;
Publisher / Repository:
https://www.iadisportal.org/ijwi/
Date Published:
Journal Name:
IADIS INTERNATIONAL JOURNAL ON WWW/INTERNET
ISSN:
1645-7641
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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