Computer Science and Engineering, Department of


First Advisor

Qiben Yan

Date of this Version

Summer 2019


@inproceedings{alhanahnah18efficient, title={Efficient Signature Generation for Classifying Cross-Architecture IoT Malware}, author={Alhanahnah, Mohannad and Lin, Qicheng and Yan, Qiben and Zhang, Ning and Chen, Zhenxiang}, booktitle={IEEE Conference on Communications and Network Security (IEEE CNS). Beijing, China}, month={May}, year={2018}, }


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Computer Science, Under the Supervision of Professor Qiben Yan. Lincoln, Nebraska: August, 2019

Copyright 2019 Qicheng Lin


The online advertising ecosystem leverages its massive data collection capability to learn the properties of users for targeted ad deliveries. Many Android app developers include ad libraries in their apps as a way of monetization. These ad libraries contain advertisements from the sell-side platforms, which collect an extensive set of sensitive information to provide more relevant advertisements for their customers. Existing efforts have investigated the increasingly pervasive private data collection of mobile ad networks over time. However, there lacks a measurement study to evaluate the scale of privacy leakage of ad networks across different geographical areas. In this work, we present a measurement study of the potential privacy leakage in mobile advertising services conducted across different locations. We develop an automated measurement system to intercept mobile traffic at different locations and perform data analysis to pinpoint data collection behaviors of ad networks at both the app-level and organization-level. With 1,100 popular apps running across 10 different locations, we perform extensive threat assessments for different ad networks. Meanwhile, we explore the ad-blockers’ behavior in the ecosystem of ad networks, and whether those ad-blockers are actually capturing the users’ private data in the meantime of blocking the ads.

We find that: the number of location-based ads tends to be positively related to the population density of locations, ad networks collect different types of data across different locations, and ad-blockers can block the private data leakage.