PGFIT: Static permission analysis of health and fitness apps in IoT programming frameworks

Nobakht, M; Sui, Y; Seneviratne, A; Hu, W

PGFIT: Static permission analysis of health and fitness apps in IoT programming frameworks

Nobakht, M Sui, Y

Seneviratne, A Hu, W

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Publisher:: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Journal of Network and Computer Applications, 2020, 152
Issue Date:: 2020-02-15

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Field	Value	Language
dc.contributor.author	Nobakht, M
dc.contributor.author	Sui, Y https://orcid.org/0000-0002-9510-6574
dc.contributor.author	Seneviratne, A
dc.contributor.author	Hu, W
dc.date.accessioned	2021-01-28T01:43:49Z
dc.date.available	2021-01-28T01:43:49Z
dc.date.issued	2020-02-15
dc.identifier.citation	Journal of Network and Computer Applications, 2020, 152
dc.identifier.issn	1084-8045
dc.identifier.issn	1095-8592
dc.identifier.uri	http://hdl.handle.net/10453/145628
dc.description.abstract	© 2020 Popular Internet of Things (IoT) programming frameworks, such as Google Fit, enable third-party developers to build apps that store and retrieve user data from a variety of data sources such as wearable devices. Most of these apps, particularly those that are health and fitness-related, collect potentially sensitive personal data and send it to cloud servers. Analogous to Android OS, IoT programming frameworks often follow similar permission model; third-party apps on IoT platforms prompt users to grant the apps the access to their private data stored on cloud servers of IoT programming frameworks. Most users have a poor understanding of why these permissions are being asked. This can often lead to unnecessary permissions being granted, which in turn grant these apps with excessive privileges. Over-privileged apps might not be harmful to users when they are used as designed, however, they can potentially be exploited by a malicious actor in a cyber security attack. This is of particular concern with health and fitness apps, which may be exploited to leak highly sensitive personal information. This paper presents PGFIT, a static permission analysis tool that precisely and efficiently identifies privilege escalation in third-party apps built on top of a popular IoT programming framework, Google Fit. PGFIT extracts the set of requested permission scopes and the set of used data types in Google Fit-enabled apps to determine whether the requested permission scopes are actually necessary. PGFIT performs graph reachability analysis on inter-procedural control flow graph. PGFIT serves as a quality assurance tool for developers and a privacy checker for app users. We evaluated PGFIT using a set of 20 popular Google Fit-enabled apps downloaded from Google Play. Our tool successfully identified the unnecessary permission scopes granted in our data set apps and found that 6 (30%) of the 20 apps are over-privileged.
dc.language	English
dc.publisher	ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.relation.ispartof	Journal of Network and Computer Applications
dc.relation.isbasedon	10.1016/j.jnca.2019.102509
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	PGFIT: Static permission analysis of health and fitness apps in IoT programming frameworks
dc.type	Journal Article
utslib.citation.volume	152
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-28T01:42:59Z
pubs.publication-status	Published
pubs.volume	152

Abstract:

© 2020 Popular Internet of Things (IoT) programming frameworks, such as Google Fit, enable third-party developers to build apps that store and retrieve user data from a variety of data sources such as wearable devices. Most of these apps, particularly those that are health and fitness-related, collect potentially sensitive personal data and send it to cloud servers. Analogous to Android OS, IoT programming frameworks often follow similar permission model; third-party apps on IoT platforms prompt users to grant the apps the access to their private data stored on cloud servers of IoT programming frameworks. Most users have a poor understanding of why these permissions are being asked. This can often lead to unnecessary permissions being granted, which in turn grant these apps with excessive privileges. Over-privileged apps might not be harmful to users when they are used as designed, however, they can potentially be exploited by a malicious actor in a cyber security attack. This is of particular concern with health and fitness apps, which may be exploited to leak highly sensitive personal information. This paper presents PGFIT, a static permission analysis tool that precisely and efficiently identifies privilege escalation in third-party apps built on top of a popular IoT programming framework, Google Fit. PGFIT extracts the set of requested permission scopes and the set of used data types in Google Fit-enabled apps to determine whether the requested permission scopes are actually necessary. PGFIT performs graph reachability analysis on inter-procedural control flow graph. PGFIT serves as a quality assurance tool for developers and a privacy checker for app users. We evaluated PGFIT using a set of 20 popular Google Fit-enabled apps downloaded from Google Play. Our tool successfully identified the unnecessary permission scopes granted in our data set apps and found that 6 (30%) of the 20 apps are over-privileged.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/145628