Biblio

Dynamic Taint Analysis (DTA) technique has been developed for analysis and understanding behavior of Android applications and privacy policy enforcement. Meanwhile, implicit information flows (IIFs) are major concern of security researchers because IIFs can evade DTA technique easily and give attackers an advantage over the researchers. Some researchers suggested approaches to the issue and developed analysis systems supporting privacy policy enforcement against IIF-accompanied attacks; however, there is still no effective technique of comprehensive analysis and privacy policy enforcement against IIF-accompanied attacks. In this paper, we propose an IIF detection technique to enforce privacy policy against IIF-accompanied attacks in Android applications. We developed a new analysis tool, called Smalien, that can discover data leakage caused by IIF-contained information flows as well as explicit information flows. We demonstrated practicability of Smalien by applying it to 16 IIF tricks from ScrubDroid and two IIF tricks from DroidBench. Smalien enforced privacy policy successfully against all the tricks except one trick because the trick loads code dynamically from a remote server at runtime, and Smalien cannot analyze any code outside of a target application. The results show that our approach can be a solution to the current attacker-superior situation.

We propose a novel framework for outsourced private function evaluation with privacy policy enforcement (OPFE-PPE). Suppose an evaluator evaluates a function with private data contributed by a data contributor, and a client obtains the result of the evaluation. OPFE-PPE enables a data contributor to enforce two different kinds of privacy policies to the process of function evaluation: evaluator policy and client policy. An evaluator policy restricts entities that can conduct function evaluation with the data. A client policy restricts entities that can obtain the result of function evaluation. We demonstrate our construction with three applications: personalized medication, genetic epidemiology, and prediction by machine learning. Experimental results show that the overhead caused by enforcing the two privacy policies is less than 10% compared to function evaluation by homomorphic encryption without any privacy policy enforcement.