Organizations are increasingly adopting the cloud to meet their business goals more cost-effectively. Cloud benefits like scalability, broad access, high availability, and cost-effectiveness provide a great incentive for organizations to move their applications to the cloud. However, concerns regarding privacy data protection remain one of the top concerns for applications migrating to the cloud. With various legislations and regulations mandating organizations to protect personal data, it is required that cloud applications and associated infrastructure are designed in a manner that provides adequate data protection. To achieve this there is a need to understand various data protection legislations, regulations, and risks faced by the cloud applications and various security controls that can be put in place to address those. Smart homes equipped with health monitoring systems have the potential to monitor the health of elderly people in their homes. In such homes, sensors are employed to monitor the activity of individuals and leverage that information to detect anomalies and raise alarms to the caretakers. However, hosting such a system in the cloud has potential privacy impacts, since health data is treated as sensitive privacy data in many regulations.  This thesis is conducted based on a use case of the deployment of an elderly health care monitoring system for municipalities in Sweden. I analyzed various regulations and privacy risks in migrating such a health monitoring system to the public cloud, the regulations captured are specific to the use case where the e-health data of Swedish citizens is captured in the cloud. The study also highlights various data protection approaches that can be employed to address the identified concerns.  In the thesis, I highlighted that data residency, data control, and the possibility of data leakage from the public cloud are among the top concerns for the municipality. I also listed various applicable data protection regulations and legislation, with “Swedish law for public access to information and secrecy” having a crucial influence on privacy data storage. I evaluated various data protection approaches to alleviate the above concerns, which include access control, anonymization, data splitting, cryptographic measures, and leveraging public cloud capabilities.

<p>In today’s world, cloud machines store an ever-increasing amount of sensitive user data, but it remains challenging to guarantee the security of our data. This is because a cloud machine’s system software—critical components like the operating system and hypervisor that can access and thus leak user data—is subject to attacks by numerous other tenants and cloud administrators. Trusted execution environments (TEEs) like Intel SGX promise to alter this landscape by leveraging a trusted CPU to create execution contexts (or enclaves) where data cannot be directly accessed by system software. Unfortunately, the protection provided by TEEs cannot guarantee complete data security. In particular, our data remains unprotected if a third-party service (e.g., Yelp) running inside an enclave is adversarial. Moreover, data can be indirectly leaked from the enclave using traditional memory side-channels.</p> <p><br></p> <p>This dissertation takes a significant stride towards strong user data protection in cloud machines using TEEs by defeating the critical threats of adversarial cloud services and memory side-channels. To defeat these threats, we systematically explore both software and hardware designs. In general, we designed software solutions to avoid costly hardware changes and present faster hardware alternatives.</p> <p><br></p> <p>We designed 4 solutions for this dissertation. Our Chancel system prevents data leaks from adversarial services by restricting data access capabilities through robust and efficient compiler-enforced software sandboxing. Moreover, our Obliviate and Obfuscuro systems leverage strong cryptographic randomization and prevent information leakage through memory side-channels. We also propose minimal CPU extensions to Intel SGX called Reparo that directly close the threat of memory side-channels efficiently. Importantly, each designed solution provides principled protection by addressing the underlying root-cause of a problem, instead of enabling partial mitigation.</p> <p><br></p> <p>Finally, in addition to the stride made by our work, future research thrust is required to make TEEs ubiquitous for cloud usage. We propose several such research directions to pursue the essential goal of strong user data protection in cloud machines.</p>