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Lifting inter-app data-flow analysis to large app sets

Journal article
Authors Florian Sattler
Alexander von Rhein
Thorsten Berger
Niklas Schalck Johansson
Mikael Mark Hardø
Sven Apel
Published in Automated Software Engineering : An International Journal
Volume 25
Issue 2
Pages 315–346
ISSN 0928-8910
Publication year 2018
Published at Department of Computer Science and Engineering (GU)
Pages 315–346
Language en
Links https://doi.org/10.1007/s10515-017-...
Keywords Android, Data-flow analysis, Inter-app communication, Variability-aware analysis, Variational data structures
Subject categories Software Engineering

Abstract

LLC Mobile apps process increasing amounts of private data, giving rise to privacy concerns. Such concerns do not arise only from single apps, which might—accidentally or intentionally—leak private information to untrusted parties, but also from multiple apps communicating with each other. Certain combinations of apps can create critical data flows not detectable by analyzing single apps individually. While sophisticated tools exist to analyze data flows inside and across apps, none of these scale to large numbers of apps, given the combinatorial explosion of possible (inter-app) data flows. We present a scalable approach to analyze data flows across Android apps. At the heart of our approach is a graph-based data structure that represents inter-app flows efficiently. Following ideas from product-line analysis, the data structure exploits redundancies among flows and thereby tames the combinatorial explosion. Instead of focusing on specific installations of app sets on mobile devices, we lift traditional data-flow analysis approaches to analyze and represent data flows of all possible combinations of apps. We developed the tool Sifta and applied it to several existing app benchmarks and real-world app sets, demonstrating its scalability and accuracy.

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