Understanding the Practices of Global Censorship through Accurate, End-to-End Measurements

Author:

Jin Lin1,Hao Shuai2,Wang Haining3,Cotton Chase1

Affiliation:

1. University of Delaware, Newark, DE, USA

2. Old Dominion University, Norfolk, VA, USA

3. Virginia Tech, Arlington, VA, USA

Abstract

It is challenging to conduct a large scale Internet censorship measurement, as it involves triggering censors through artificial requests and identifying abnormalities from corresponding responses. Due to the lack of ground truth on the expected responses from legitimate services, previous studies typically require a heavy, unscalable manual inspection to identify false positives while still leaving false negatives undetected. In this paper, we propose Disguiser, a novel framework that enables end-to-end measurement to accurately detect the censorship activities and reveal the censor deployment without manual efforts. The core of Disguiser is a control server that replies with a static payload to provide the ground truth of server responses. As such, we send requests from various types of vantage points across the world to our control server, and the censorship activities can be recognized if a vantage point receives a different response. In particular, we design and conduct a cache test to pre-exclude the vantage points that could be interfered by cache proxies along the network path. Then we perform application traceroute towards our control server to explore censors' behaviors and their deployment. With Disguiser, we conduct 58 million measurements from vantage points in 177 countries. We observe 292 thousand censorship activities that block DNS, HTTP, or HTTPS requests inside 122 countries, achieving a 10^-6 false positive rate and zero false negative rate. Furthermore, Disguiser reveals the censor deployment in 13 countries.

Funder

National Science Foundation

Army Research Office

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Safety, Risk, Reliability and Quality,Computer Science (miscellaneous)

Reference54 articles.

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2. Alexa. [n.d.]. https://www.alexa.com/topsites . Alexa. [n.d.]. https://www.alexa.com/topsites .

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