Pathfinder: Exploring Path Diversity for Assessing Internet Censorship Inconsistency
<|reference_start|>Pathfinder: Exploring Path Diversity for Assessing Internet Censorship Inconsistency: Internet censorship is typically enforced by authorities to achieve information control for a certain group of Internet users. So far existing censorship studies have primarily focused on country-level characterization because (1) in many cases, censorship is enabled by governments with nationwide policies and (2) it is usually hard to control how the probing packets are routed to trigger censorship in different networks inside a country. However, the deployment and implementation of censorship could be highly diverse at the ISP level. In this paper, we investigate Internet censorship from a different perspective by scrutinizing the diverse censorship deployment inside a country. Specifically, by leveraging an end-to-end measurement framework, we deploy multiple geo-distributed back-end control servers to explore various paths from one single vantage point. The generated traffic with the same domain but different control servers' IPs could be forced to traverse different transit networks, thereby being examined by different censorship devices if present. Through our large-scale experiments and in-depth investigation, we reveal that the diversity of Internet censorship caused by different routing paths inside a country is prevalent, implying that (1) the implementations of centralized censorship are commonly incomplete or flawed and (2) decentralized censorship is also common. Moreover, we identify that different hosting platforms also result in inconsistent censorship activities due to different peering relationships with the ISPs in a country. Finally, we present extensive case studies in detail to illustrate the configurations that lead to censorship inconsistency and explore the causes.<|reference_end|>
@article{liang2024pathfinder:,
    title={Pathfinder: Exploring Path Diversity for Assessing Internet Censorship
  Inconsistency},
    author={Xiaoqin Liang, Guannan Liu, Lin Jin, Shuai Hao, Haining Wang},
    journal={arXiv preprint arXiv:2407.04213},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04213},
    primaryClass={cs.CR cs.NI}
}