Till sidans topp

Sidansvarig: Webbredaktion
Sidan uppdaterades: 2012-09-11 15:12

Tipsa en vän
Utskriftsversion

Lifting inter-app data-fl… - Göteborgs universitet Till startsida
Webbkarta
Till innehåll Läs mer om hur kakor används på gu.se

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

Artikel i vetenskaplig tidskrift
Författare Florian Sattler
Alexander von Rhein
Thorsten Berger
Niklas Schalck Johansson
Mikael Mark Hardø
Sven Apel
Publicerad i Automated Software Engineering : An International Journal
Volym 25
Nummer/häfte 2
Sidor 315–346
ISSN 0928-8910
Publiceringsår 2018
Publicerad vid Institutionen för data- och informationsteknik (GU)
Sidor 315–346
Språk en
Länkar https://doi.org/10.1007/s10515-017-...
Ämnesord Android, Data-flow analysis, Inter-app communication, Variability-aware analysis, Variational data structures
Ämneskategorier Programvaruteknik

Sammanfattning

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.

Sidansvarig: Webbredaktion|Sidan uppdaterades: 2012-09-11
Dela:

På Göteborgs universitet använder vi kakor (cookies) för att webbplatsen ska fungera på ett bra sätt för dig. Genom att surfa vidare godkänner du att vi använder kakor.  Vad är kakor?