Measuring the blocking of AN.ON users by popular websites through web scraping

Bachelor thesis (2021)

Authors

J. Mulder Electrical Engineering, Mathematics and Computer Science

Contributors

S. Roos Data-Intensive Systems - EEMCS (mentor)
D. Spinellis Software Engineering - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Jurgen Mulder
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Published Date

02-07-2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Users of anonymity networks face differential treatment and sometimes get blocked by websites, it is currently unclear how common this blocking is. This research aims to provide an overview of how common this blocking is while utilizing the AN.ON anonymity network. The analysis is accomplished by utilizing automated web scraping and processing to recognise and classify blocks by comparing them to a control connection. This process and software can be used and extended to analyze and compare any two connections. The scope is limited to the one thousand most popular websites according to the Alexa rating. Different kinds of blocks were identified and automatically recognised in processing, though manual verification is still required. Evidence is found and presented that there is a significant amount of blocking, occurring on approximately 23% of the analyzed domains. There is also a significant difference in blocking between using different cascades.

Files

RP_Blocking_ANON_Final2.pdf
(pdf | 0.358 Mb)
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