Main content

Encrypted Image Classification for Improving the Security of Cloud-Based Intelligent Transportation Systems

Show full item record

Title: Encrypted Image Classification for Improving the Security of Cloud-Based Intelligent Transportation Systems
Author: Lidkea, Viktor
Department: School of Engineering
Program: Engineering
Advisor: Muresan, RaduAl-Dweik, Arafat
Abstract: Cloud computing technology is integral to the advancement of intelligent transportation systems. Integrating cloud computing into intelligent transportation systems needs to proceed with caution however, as cloud computing introduces new layers of security risks. As intelligent transportation systems generally rely on captured images of private citizens, security of these images is paramount. In this thesis, we propose an efficient system for improving the security of a cloud-based intelligent transportation system built with road side units, and a data collection and analysis server. A convolutional neural network is used to classify encrypted images obtained by roadside units based on the type of vehicle on the road in real-time, leaving personal information in these images hidden throughout the process. The proposed system never fully decrypts the collected images, thus protecting drivers’ personal information, such as location, license plate, and vehicle contents. As the system never needs to fully decrypt the images, the system increases efficiency compared to a system which fully decrypts the images for analysis. The results show improved computational performance in comparison with a fully decrypting system, while keeping the data secure.
URI: http://hdl.handle.net/10214/17479
Date: 2019-09-06
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.


Files in this item

Files Size Format View Description
Lidkea_Viktor_201909_Masc.pdf 9.703Mb PDF View/Open ITS Security Thesis Viktor Lidkea

This item appears in the following Collection(s)

Show full item record