Practical Verifiable Computation-A MapReduce Case Study

Public cloud vendors have been offering a variety of big data computing services on their clouds. However, runtime integrity is one of the major security concerns that hinder the wide adoption of those services. In this paper, we focus on MapReduce, a popular big data computing framework, and propose the runtime integrity audition (RIA), a solution that remotely verifies the runtime integrity of MapReduce applications. RIA records the runtime variable values of the MapReduce application on the public cloud and checks those values against the application's code on the private cloud. By doing so, RIA protects the runtime integrity of MapReduce applications. Based on the idea of RIA, we developed a prototype system, called MR Auditor, and tested its applicability and performance with several Hadoop applications. Our experimental results showed that MR Auditor is a general tool that can efficiently audit the runtime integrity of all the MapReduce applications that we tested. In addition, MR Auditor incurs a moderate performance overhead. For example, when verifying the Word Count application, a proper parameter setting of MR Auditor incurs 1% of extra execution time on the public cloud and 14% of extra execution time on the private cloud.