Date of Award


Degree Name

Master of Science


Electrical and Computer Engineering


Sai Mounika Errapotu


Over the last decade, billions of devices have been developed to run on the Internet of Things (IoT) domain. IoT has been embedded into everyday tasks so much that the number of devices has outpaced the global population. Some of the main areas of IoT include healthcare, industry, communications, transportation, logistics, and environmental awareness, to name a few. With this greater demand for devices comes an even greater demand for privacy and security. The three main principles of security include confidentiality, integrity, and availability. However, there is no “one size fits all” solution regarding privacy or security. When applied across IoT in real-time settings, hardware limitations and computational overhead must be considered. Most of the methods in IoT security focus on communication between devices, but not on on-device communication and computation, which are critical vulnerabilities in IoT ecosystems. In this work, we have explored differential privacy to address vulnerabilities in different elements of IoT ecosystems, which include on-device computation, communication, and cloud. Differential privacy has often been used in analytics to strengthen user privacy through noise addition, to prevent traceback to sensitive data sources during analysis. In this work, we test for differential privacy implementation in analytical settings and beyond analytical settings, especially for its feasible implementation on hardware. This thesis explores some of the layered security approaches that are currently being used with differential privacy in IoT such as machine learning, federated learning, and secure processing. It also explores the application of differential privacy in hardware-isolated trusted execution environments. This thesis tests and analyzes implementation aspects of differential privacy in IoT systems, its limitations, and potential improvements in software as well as hardware.




Recieved from ProQuest

File Size

78 p.

File Format


Rights Holder

Nicholas Anthony Lopez

Available for download on Friday, January 16, 2026