QuantiC: Distance Metrics for Evaluating Multi-Tenancy Threats in Public Cloud
Taous Madi, Mengyuan Zhang, Yosr Jarraya, Amir Alimohammadifar, Makan Pourzandi, Lingyu Wang, Mourad Debbabi · 2018
As a cornerstone of cloud computing, multi-tenancy brings not only the benefit of resource sharing but also additional security implications. To achieve an optimal trade-off between security and resource sharing, cloud providers are obliged to evaluate the potential threats related to multi-tenancy. However, quantitative approaches for evaluating those threats are largely missing in existing works. In this paper, we propose a set of multi-level distance metrics that quantify the proximity of tenants' virtual resources inside a cloud. Those metrics are defined based on the configuration and deployment in a cloud, such that a cloud provider may apply them to evaluate the risk related to potential multi-tenancy attacks. We conduct case studies and experiments on both real and fictitious clouds. The obtained results show the effectiveness and applicability of our metrics. We further implement our metrics in OpenStack and show how they can be applied for distance auditing.