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Title:Hardware-assisted privacy enforcement in commercial drone-as-a-service applications
Author(s):Liu, Tianyuan
Director of Research:Nahrstedt, Klara
Doctoral Committee Chair(s):Nahrstedt, Klara
Doctoral Committee Member(s):Gunter, Carl A.; Bates, Adam; Danilov, Claudiu B.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Drone
Privacy
Hardware Security
Abstract:Unmanned Aerial Vehicles (UAVs), also known as drones, have become more popular in commercial activities than before. Many third-party drone service providers offer their drones and pilots to assist client businesses in a variety of missions. Such a business model is also known as Drone-as-a-Service (DaaS) model. However, the adoption of DaaS applications has been severely impeded due to the potential safety and privacy risks of drones. A malicious drone can fly over residential area and spy on citizens' information. When such drones are equipped with sensors, they can also eavesdrop or devastate private data that goes through wireless sensors. The public damage of the drones are enlarged in DaaS applications because the client often has very limited transparency on the hired drones. To tackle the above challenges, I present Hardware-Assisted Privacy Enforcement (HAPE) as a potential solution for the privacy issues in DaaS applications. The design of HAPE relies on the hardware-assisted security components, which are installed on the drones, to act as an external source of trust. Therefore, it can be used in various situations such as encrypting sensitive data, authorizing private access, and generating provenance. Based on HAPE, I design three systems to enhance the location privacy, the data privacy and the assignment and management of the DaaS applications. The experiments on these prototypes confirm that HAPE is a viable solution to mitigate the privacy threat of drones in DaaS applications.
Issue Date:2021-07-16
Type:Thesis
URI:http://hdl.handle.net/2142/113059
Rights Information:Copyright 2021 Tianyuan Liu
Date Available in IDEALS:2022-01-12
Date Deposited:2021-08


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