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Anaconda: Detecting private-information leaks in Android apps using static data-flow analysis

Groenewold, S and Winter, K.L and Veldthuis, J (2013) Anaconda: Detecting private-information leaks in Android apps using static data-flow analysis. Bachelor's Thesis, Computing Science.

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With the advent of the smartphone, new ways of communicating and connecting with the internet have opened up. For many people, smartphones have replaced their watch, their address book, and their calendar. This means that the average smartphone has a lot of private information stored on it, for example: SMS or MMS messages, what web-pages were visited in the browser, or the call history. Unfortunately, it is not clear what apps on smartphones do with this information, and whether this information is used maliciously. Anaconda addresses these issues by using static data-flow analysis on Android apps, reporting whether these apps request private information, and reporting whether this information is sent to remote servers (i.e. leaked). In 14 popular apps, Anaconda found 572 requests of private information, of which 243 cases led to a possible leak. While some of these information leaks are legitimate uses, a large number of the leaks are not legitimate or at least suspicious. Most leaked information is either sent to ad servers or the app developers’ servers, and may not even be used by the app itself.

Item Type: Thesis (Bachelor's Thesis)
Degree programme: Computing Science
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 15 Feb 2018 07:53
Last Modified: 15 Feb 2018 07:53

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