Look What’s There! Utilizing the Internet’s Existing Data for Censorship Circumvention with OPPRESSION
Sebastian Zillien, Tobias Schmidbauer, Mario Kubek, Jörg Keller, Steffen Wendzel · 2024
An ongoing challenge in censorship circumvention is optimizing the stealthiness of communications, enabled by covert channels. Recently, a new variant called history covert channels has been proposed. Instead of modifying or mimicking legitimate data, such channels solely point to observed data matching secret information. This approach reduces the amount of secret data a sender explicitly must transfer and thus limits detectability. However, the only published history channel is only suitable for special scenarios due to severe limitations in terms of bandwidth. We propose a significant performance enhancement of history covert channels that allows their use in real-world scenarios through utilizing the content of online social media and online archives. Our approach, which we call OPPRESSION (Open-knowledge Compression), takes advantage of the massive amounts of textual data on the Internet that can be referenced by short pointer messages. Broadly, OPPRESSION can be considered a novel encoding strategy for censorship circumvention.