Toward protecting control flow confidentiality in cloud-based computation

Abstract

Cloud based computation services have grown in popularity in recent years. Cloud users can deploy an arbitrary computation cluster to public clouds and execute their programs on that remote cluster to reduce infrastructure investment and maintenance costs. However, how to leverage cloud resources while keeping the computation confidential is a new challenge to be explored. In this paper, we propose runtime control flow obfuscation (RCFO) to protect the control flow confidentiality of outsourced programs. RCFO transforms an outsourced program into two parts: the public program running on the untrusted public cloud and the private program running on the trusted private cloud. By hiding parts of the control flow information in the private program and inserting fake branch statements into the public program, RCFO raises the bar for static and dynamic analysis-based reverse engineering attacks. Based on RCFO, we implement a system called MRDisguiser to protect cloud-based MapReduce services. We perform experiments on a real MapReduce service, Amazon Elastic MapReduce. The experimental results indicate that MRDisguiser is compatible with current cloud-based MapReduce services, and incurs moderate performance overhead. Specifically, when the obfuscation degree increases from 0 to 1.0, the average performance overhead is between 14.9% and 33.2%.