Inhaltszusammenfassung

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 suitabl...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. We further present and evaluate our open source proof-of-concept implementation of OPPRESSION that can transfer secret data by pointing to popular online media, such as Twitter (now “X”), news websites, Wikipedia entries, and online books. The pointer itself is transmitted through existing censorship circumvention systems. Our approach minimizes the amount of traffic to be concealed in comparison to existing works, even in comparison to compression.» weiterlesen» einklappen

Autoren

Klassifikation

DFG Fachgebiet:
Informatik

DDC Sachgruppe:
Informatik