"Hard to understand, easy to ignore:" an automated approach to predict mobile app permission requests: student research abstract

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a novel automated approach to predict the potential privacy sensitive permission requests by mobile apps. Based on machine learning (ML) and natural language processing (NLP) techniques, personal data access and collection practices mentioned in app privacy policy text are analyzed to predict the required permission requests. Further, the predicted list of permission requests is compared with the real permission requests to check whether there is any mismatch. We further propose user interface designs to map mobile app permission requests to understandable language definitions for the end user. The combination of these concepts provides users with special knowledge about data protection practice and behavior of apps based on the analysis of privacy policy text and permission declaration which are otherwise difficult to analyze. Initial results demonstrate the capability of our approach in prediction of app permission requests. Also, by exploiting our already proposed app behavior analyzer tool, we investigated the correlation between what mobile apps do in reality and what they promise in their privacy policy text resulting in a positive correlation.
Original languageEnglish
Title of host publicationSAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages1979-1982
Number of pages4
ISBN (Print)9781450359337
DOIs
Publication statusPublished - 8 Apr 2019
Externally publishedYes
Event34th ACM/SIGAPP Symposium On Applied Computing - Limassol, Cyprus
Duration: 8 Apr 2019 → …
https://www.sigapp.org/sac/sac2019/

Conference

Conference34th ACM/SIGAPP Symposium On Applied Computing
Abbreviated titleACM SAC 2019
CountryCyprus
CityLimassol
Period8/04/19 → …
Internet address

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