In cloud computing customers upload data and computation to cloud providers. As they cede their data to the cloud provider, they may cede data confidentiality. We develop SecureMR, a system that analyzes and transforms MapReduce programs to operate over encrypted data. SecureMR makes use of partially homomorphic encryption and a trusted client. We evaluate SecureMR on a set of MapReduce benchmarks.

We propose a type-based taint analysis for Android. Concretely, we present DFlow, a context-sensitive information flow type system, and DroidInfer, the corresponding type inference analysis for detecting privacy leaks in Android apps. We present novel techniques for error reporting based on CFL-reachability, as well as novel techniques for handling of Android-specific features, including libraries, multiple entry points and callbacks, and inter-component communication. Empirical results show that our approach is scalable and precise. DroidInfer scales well in terms of time and memory and has false-positive rate of 15.7%. It detects privacy leaks in apps from the Google Play Store and in known malware.