UC Irvine

Preserving data privacy is a formidable challenge in today’s interconnected and data-centric world. Individuals are surrounded by “smart” devices that collect and generate massive amounts of sensitive data. Moreover, organizations collect personalized data, including private information, to provide more functionalities and better quality for their data-driven services. Therefore, ensuring data privacy throughout its lifecycle, i.e., from generation to consumption, is paramount.To this end, this dissertation tackles several challenges to attain such end-to-end data privacy. We first investigate lower-end devices to preserve data privacy from its generation, and propose two secure architectures: one for mid-range devices with memory management unit and the other for low-end devices with no security features. Then, we revisit cryptographic computing, a promising privacy-enhancing technology for data in use, focusing on input correctness, generalized adversary models, and challenges in real-world applications.