Constructing Secure MapReduce Framework in Cloud-based Environment

Abstract

MapReduce, a parallel computing paradigm, has been gaining popularity in recent years as cloud vendors offer MapReduce computation services on their public clouds. However, companies are still reluctant to move their computations to the public cloud due to the following reason: In the current business model, the entire MapReduce cluster is deployed on the public cloud. If the public cloud is not properly protected, the integrity and the confidentiality of MapReduce applications can be compromised by attacks inside or outside of the public cloud. From the result integrity’s perspective, if any computation nodes on the public cloud are compromised,thosenodes can return incorrect task results and therefore render the final job result inaccurate. From the algorithmic confidentiality’s perspective, when more and more companies devise innovative algorithms and deploy them to the public cloud, malicious attackers can reverse engineer those programs to detect the algorithmic details and, therefore, compromise the intellectual property of those companies. In this dissertation, we propose to use the hybrid cloud architecture to defeat the above two threats. Based on the hybrid cloud architecture, we propose separate solutions to address the result integrity and the algorithmic confidentiality problems. To address the result integrity problem, we propose the Integrity Assurance MapReduce (IAMR) framework. IAMR performs the result checking technique to guarantee high result accuracy of MapReduce jobs, even if the computation is executed on an untrusted public cloud. We implemented a prototype system for a real hybrid cloud environment and performed a series of experiments. Our theoretical simulations and experimental results show that IAMR can guarantee a very low job error rate, while maintaining a moderate performance overhead. To address the algorithmic confidentiality problem, we focus on the program control flow and propose the Confidentiality Assurance MapReduce (CAMR) framework. CAMR performs the Runtime Control Flow Obfuscation (RCFO) technique to protect the predicates of MapReduce jobs. We implemented a prototype system for a real hybrid cloud environment. The security analysis and experimental results show that CAMR defeats static analysis-based reverse engineering attacks, raises the bar for the dynamic analysis-based reverse engineering attacks, and incurs a modest performance overhead.