A Framework for Privacy-Aware Computing on Hybrid Clouds with Mixed-Sensitivity Data

Security and privacy have long been the primary concerns of cloud computing platforms. Hybrid clouds provide potentials for handling data separately based on their sensitivity, harnessing the heterogeneous architecture. In this paper, we design and implement a privacy-aware framework to address data privacy challenges by supporting sensitive data segregation on hybrid clouds. Specifically, we model data sensitivity in a comprehensive and dynamic manner using a set of tagging mechanisms, which include a coarse-grained file level tagging, a fine-grained line level tagging, temporal and spatial tagging. The framework can also process data dynamically generated on-the-fly. We demonstrate the effectiveness of this framework using a big data application, and the experimental results show that the privacy-aware framework successfully enables data sensitivity protection while providing good performance.