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See detailNegative Results of Fusing Code and Documentation for Learning to Accurately Identify Sensitive Source and Sink Methods An Application to the Android Framework for Data Leak Detection
Samhi, Jordan UL; Kober, Kober; Kabore, Abdoul Kader UL et al

in Negative Results of Fusing Code and Documentation for Learning to Accurately Identify Sensitive Source and Sink Methods An Application to the Android Framework for Data Leak Detection (2023, March)

Almost two-thirds of the population owns a mobile phone. Given that there is a profusion of mobile applications that manipulate all sorts of data, privacy-related concerns arise more and more. New ... [more ▼]

Almost two-thirds of the population owns a mobile phone. Given that there is a profusion of mobile applications that manipulate all sorts of data, privacy-related concerns arise more and more. New regulations such as the General Data Protection Regulation (GDPR) provide rules for which developers must comply when their apps process sensitive and/or private data. Ensuring that no such data is leaked without the consent of the user is a primary objective in each GDPR compliance check. Researchers have proposed sophisticated approaches to track sensitive data within mobile apps, all of which rely on specific lists of sensitive source and sink methods. The data flow analysis results greatly depend on these lists' quality. Previous approaches either used incomplete hand-written lists and quickly became outdated or relied on machine learning. The latter, however, leads to numerous false positives, as we show. This paper introduces CoDoC that aims to revive the machine-learning approach to precisely identify the privacy-related source and sink API methods. In contrast to previous approaches, CoDoC uses deep learning techniques and combines the source code with the documentation of API methods. Firstly, we propose novel definitions that clarify the concepts of taint analysis, source, and sink methods. Secondly, based on these definitions, we build a new ground truth of Android methods representing sensitive source, sink, and neither methods that will be used to train our classifier. We evaluate CoDoC and show that, on our validation dataset, it achieves a precision, recall, and F1 score of 91%, outperforming the state-of-the-art SuSi. However, similarly to existing tools, we show that in the wild, i.e., with unseen data, CoDoC performs poorly and generates many false-positive results. Our findings suggest that machine-learning models for abstract concepts such as privacy fail in practice despite good lab results. To encourage future research, we release all our artifacts to the community. [less ▲]

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See detailIccTA: Detecting Inter-Component Privacy Leaks in Android Apps
Li, Li UL; Bartel, Alexandre; Bissyande, Tegawendé François D Assise UL et al

in 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE 2015) (2015)

Shake Them All is a popular "Wallpaper" application exceeding millions of downloads on Google Play. At installation, this application is given permission to (1) access the Internet (for updating ... [more ▼]

Shake Them All is a popular "Wallpaper" application exceeding millions of downloads on Google Play. At installation, this application is given permission to (1) access the Internet (for updating wallpapers) and (2) use the device microphone (to change background following noise changes). With these permissions, the application could silently record user conversations and upload them remotely. To give more confidence about how Shake Them All actually processes what it records, it is necessary to build a precise analysis tool that tracks the flow of any sensitive data from its source point to any sink, especially if those are in different components. Since Android applications may leak private data carelessly or maliciously, we propose IccTA, a static taint analyzer to detect privacy leaks among components in Android applications. IccTA goes beyond state-of-the-art approaches by supporting inter-component detection. By propagating context information among components, IccTA improves the precision of the analysis. IccTA outperforms existing tools on two benchmarks for ICC-leak detectors: DroidBench and ICC-Bench. Moreover, our approach detects 534 ICC leaks in 108 apps from MalGenome and 2,395 ICC leaks in 337 apps in a set of 15,000 Google Play apps. [less ▲]

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See detailI know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis
Li, Li UL; Bartel, Alexandre UL; Klein, Jacques UL et al

Report (2014)

Android applications may leak privacy data carelessly or maliciously. In this work we perform inter-component data-flow analysis to detect privacy leaks between components of Android applications. Unlike ... [more ▼]

Android applications may leak privacy data carelessly or maliciously. In this work we perform inter-component data-flow analysis to detect privacy leaks between components of Android applications. Unlike all current approaches, our tool, called IccTA, propagates the context between the components, which improves the precision of the analysis. IccTA outperforms all other available tools by reaching a precision of 95.0% and a recall of 82.6% on DroidBench. Our approach detects 147 inter-component based privacy leaks in 14 applications in a set of 3000 real-world applications with a precision of 88.4%. With the help of ApkCombiner, our approach is able to detect inter-app based privacy leaks. [less ▲]

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See detailHighly precise taint analysis for Android applications
Fritz, Christian; Arzt, Steven; Rasthofer, Siegfried et al

Report (2013)

Today’s smart phones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by malicious apps that exploit their given privileges to steal such sensitive ... [more ▼]

Today’s smart phones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by malicious apps that exploit their given privileges to steal such sensitive data, or to track users without their consent or even the users noticing. Dynamic program analyses fail to discover such malicious activity because apps have learned to recognize the analyses as they execute. In this work we present FlowDroid, a novel and highly precise taint analysis for Android applications. A precise model of Android’s lifecycle allows the analysis to properly handle callbacks, while context, flow, field and objectsensitivity allows the analysis to track taints with a degree of precision unheard of from previous Android analyses. We also propose DroidBench, an open test suite for evaluating the e↵ectiveness and accuracy of taint-analysis tools specifically for Android apps. As we show through a set of experiments using SecuriBench Micro, DroidBench and a set of well-known Android test applications, our approach finds a very high fraction of data leaks while keeping the rate of false positives low. On DroidBench, our approach achieves 93% recall and 86% precision, greatly outperforming the commercial tools AppScan Source and Fortify SCA. [less ▲]

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