2015 Theses Doctoral

Information Flow Auditing in the Cloud

Zavou, Angeliki

As cloud technology matures and trendsetters like Google, Amazon, Microsoft, Apple, and VMware have become the top-tier cloud services players, public cloud services have turned mainstream for individual users. In this work, I propose a set of techniques that can be used as the basis for alleviating cloud customers' privacy concerns and elevating their condence in using the cloud for security-sensitive operations as well as trusting it with their sensitive data. The main goal is to provide cloud customers' with a reliable mechanism that will cover the entire path of tracking their sensitive data, while they are collected and used by cloud-hosted services, to the presentation of the tracking results to the respective data owners. In particular, my design accomplishes this goal by retrofitting legacy applications with data flow tracking techniques and providing the cloud customers with comprehensive information flow auditing capabilities. For this purpose, we created CloudFence, a cloud-wide fine-grained data flow tracking (DFT) framework, that sensitive data in well-defined domains, offering additional protection against inadvertent leaks and unauthorized access.