Paper 2026/1178

Sensing Censorship and Censuring Censors with Censorship-Evident Publishing Systems

Abstract

Censorship has always existed, serving both to prevent harms and to inflict them by chilling speech, suppressing organizing, and withholding inconvenient facts and ideas; most technical work aims to prevent all forms of censorship --- the "good", the "bad", and everything in between. We study the complementary, rarely explored goal of making any censorship attempt transparent. We formalize censorship-evident publishing systems (CEPS), protocols that force both overt and covert takedowns to yield transferable evidence. We also provide a CEPS instantiation with Streisand, a proof-of-concept deployment that combines a blockchain-backed timestamp oracle, private information retrieval (PIR)-based anonymous queries to prevent extraction attempts from being conspicuous, and probabilistic Merkle-witness retrieval to produce compact censorship proofs. We present performance evaluations on a 1.3 GiB Enron-derived dataset with regex-based PII redaction to model realistic censorship, and demonstrate that a background daemon can detect heavy censorship after a small number of post-censorship queries, making Streisand an effective auditing mechanism rather than interactive file retrieval. We also discuss design trade-offs (proof size vs. computation, PIR sufficiency vs. necessity), scalability limits, and how CEPS complements existing transparency practices, with Streisand as a starting point for CEPS deployments.