Today, security is a requirement for smartphone operating systems that are used to store and handle sensitive information. How- ever, smartphone users usually download third-party applications that can leak personal data without user authorization. For this reason, the dynamic taint analysis mechanism is used to control the manipulation of private data by third-party apps [9]. But this technique does not detect control flows. In particular, untrusted applications can circumvent An- droid system and get privacy sensitive information through control flows. In this paper, we propose a hybrid approach that combines static and dynamic analysis to propagate taint along control dependencies in An- droid system. To evaluate the effectiveness of our approach, we analyse 27 free Android applications. We found that 14 of these applications use control flows to transfer sensitive data. We successfully detect that 8 of them leaked private information. Our approach creates 19% performance overhead that is due to the propagation of taint in the control flow. By using our approach, it becomes possible to detect leakage of personal data through control flows.