Structural cloud audits that protect private information

As organizations and individuals have begun to rely more and more heavily on cloud-service providers for critical tasks, cloud-service reliability has become a top priority. It is natural for cloud-service providers to use redundancy to achieve reliability. For example, a provider may replicate critical state in two data centers. If the two data centers use the same power supply, however, then a power outage will cause them to fail simultaneously; replication per se does not, therefore, enable the cloud-service provider to make strong reliability guarantees to its users. Zhai et al.[socc-submission] present a system, which they refer to as a structural-reliability auditor (SRA), that uncovers common dependencies in seemingly disjoint cloud-in\-fra\-struc\-tu\-ral components (such as the power supply in the example above) and quantifies the risks that they pose. In this paper, we focus on the need for structural-reliability auditing to be done in a privacy-preserving manner. We present a privacy-preserving structural-reliability auditor (P-SRA), discuss its privacy properties, and evaluate a prototype implementation built on the Sharemind SecreC platform[SecreC]. P-SRA is an interesting application of secure multi-party computation (SMPC), which has not often been used for graph problems. It can achieve acceptable running times even on large cloud structures by using a novel data-partitioning technique that may be useful in other applications of SMPC.