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

Apps on mobile phones manipulate all sorts of data, including sensitive data, leading to privacy related concerns. Recent regulations like the European GDPR provide rules for the processing of personal and sensitive data, like that no such data may be leaked without the consent of the user.

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 Previous approaches either used incomplete hand-written lists and quickly became outdated or relied on machine learning.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.