Biblio

Static taint analyses are widely-applied techniques to detect taint flows in software systems. Although they are theoretically conservative and de-signed to detect all possible taint flows, static taint analyses almost always exhibit false negatives due to a variety of implementation limitations. Dynamic programming language features, inaccessible code, and the usage of multiple programming languages in a software project are some of the major causes. To alleviate this problem, we developed a novel approach, DySTA, which uses dynamic taint analysis results as additional sources for static taint analysis. However, naïvely adding sources causes static analysis to lose context sensitivity and thus produce false positives. Thus, we developed a hybrid context matching algorithm and corresponding tool, ConDySTA, to preserve context sensitivity in DySTA. We applied REPRODROID [1], a comprehensive benchmarking framework for Android analysis tools, to evaluate ConDySTA. The results show that across 28 apps (1) ConDySTA was able to detect 12 out of 28 taint flows which were not detected by any of the six state-of-the-art static taint analyses considered in ReproDroid, and (2) ConDySTA reported no false positives, whereas nine were reported by DySTA alone. We further applied ConDySTA and FlowDroid to 100 top Android apps from Google Play, and ConDySTA was able to detect 39 additional taint flows (besides 281 taint flows found by FlowDroid) while preserving the context sensitivity of FlowDroid.

The Android mobile platform supports billions of devices across more than 190 countries around the world. This popularity coupled with user data collection by Android apps has made privacy protection a well-known challenge in the Android ecosystem. In practice, app producers provide privacy policies disclosing what information is collected and processed by the app. However, it is difficult to trace such claims to the corresponding app code to verify whether the implementation is consistent with the policy. Existing approaches for privacy policy alignment focus on information directly accessed through the Android platform (e.g., location and device ID), but are unable to handle user input, a major source of private information. In this paper, we propose a novel approach that automatically detects privacy leaks of user-entered data for a given Android app and determines whether such leakage may violate the app's privacy policy claims. For evaluation, we applied our approach to 120 popular apps from three privacy-relevant app categories: finance, health, and dating. The results show that our approach was able to detect 21 strong violations and 18 weak violations from the studied apps.

Mobile applications frequently access sensitive personal information to meet user or business requirements. Because such information is sensitive in general, regulators increasingly require mobile-app developers to publish privacy policies that describe what information is collected. Furthermore, regulators have fined companies when these policies are inconsistent with the actual data practices of mobile apps. To help mobile-app developers check their privacy policies against their apps' code for consistency, we propose a semi-automated framework that consists of a policy terminology-API method map that links policy phrases to API methods that produce sensitive information, and information flow analysis to detect misalignments. We present an implementation of our framework based on a privacy-policy-phrase ontology and a collection of mappings from API methods to policy phrases. Our empirical evaluation on 477 top Android apps discovered 341 potential privacy policy violations.