ACM SIGCOMM 2015's program committee accepted the Encore paper with an unprecedented 'signing statement' after heated ethical debate. The committee's core objections were: (1) users accessing censored URLs might face repercussions in regimes without due process; (2) most users under censorship would be unlikely to consent to the measurements; and (3) unlike ad-tracker third-party requests, Encore requests do not reflect any user intent.

The Encore system collected censorship measurements from 88,260 distinct IP addresses across 170 countries over seven months via installations by at least 17 volunteer website operators. China, India, the United Kingdom, and Brazil each contributed at least 1,000 measurements; Egypt, South Korea, Iran, Pakistan, Turkey, and Saudi Arabia each contributed more than 100.

Introduction evaluation

The paper identifies a structural conflict between Internet research's scalability imperative — where a project processing millions of devices is considered superior — and human-subjects ethics frameworks designed to minimize the number of people exposed to risk. Under U.S. law, Encore is compliant because it exploits known, intentional web functionality (the same-origin policy's cross-origin request mechanism) and provides an opt-out mechanism, but the authors note this compliance does not transfer to all jurisdictions where measurements occur.

Legal Compliance / Discussion policy

To mitigate harm, Encore restricted its URL list to Twitter, Facebook, and YouTube on the grounds that widgets from these domains appear in ordinary web browsing, making Encore-induced cross-origin requests statistically indistinguishable from normal traffic. The authors argued that this renders the risk comparable to baseline browsing, though the SIGCOMM committee disputed whether contextual equivalence with ad-tracking constitutes adequate ethical justification.

Mitigating Harm policy

Encore's architecture turns ordinary web visitors into measurement vantage points, which the researchers argue prevents censors from detecting and disabling dedicated measurement probes. However, this benefit comes with the trade-off that the individuals whose browsers are co-opted face potential legal or physical risk that differs by country and by the specific censored content accessed.

Background / Analysis policy

The largest single source of censored domains in the GNL is MESA lab's SNI monitoring dataset (E21-SNI-Top200w.txt) containing 57,362 censored domains, and E21-SNI-Top120W-20221020.txt with 36,467 domains—totaling over 93K domains from network tap data alone for a single country (E21 = Ethiopia per InterSecLab attribution). A separate Xinjiang dataset (XJ-CUCC-SNI-Top200w.txt) contains 13,604 domains. These datasets "do not seem to come from popular domain lists, and instead appear to be gathered from network taps," confirming that Geedge builds censorship target lists directly from passive traffic observation.

2026-sheffey-geedge §4.2, Table 3 dpisni-blockingmeasurement-platform

Of 6,915,266 domains extracted from the 572 GiB Geedge Networks Leak (GNL), 298,955 censored domains (93.7% of all GNL-censored domains) appear in neither Tranco top-1M nor CitizenLab test lists. Measurements across China (Guangzhou/Nanjing), Myanmar, Pakistan, and Algeria confirmed censorship via DNS injection and SNI-based TLS connection termination. The GNL covers 25–62% of Tranco-censored domains across countries, showing substantial but incomplete overlap. This vendor-side ground truth reveals a censorship surface roughly two orders of magnitude larger than curated academic test lists.

2026-sheffey-geedge §4.1, Table 2 dpisni-blockingdns-poisoningmeasurement-platform

CensorAlert aggregates censorship signals from heterogeneous open sources — including OONI, Cloudflare Radar, NetBlocks, GitHub Issues of circumvention tools (Hysteria, Xray), Net4People BBS, NTC Party forum, Mastodon, X, Telegram channels, and arXiv — normalizing each item into a common schema preserving timestamp, source, raw data, and provenance with a link to the original content.

2026-zohaib-extended §2 (Data Sources) measurement-platform

Systematic measurement platforms (OONI, Censored Planet, Cloudflare Radar, NetBlocks) have inherent blind spots due to geographic coverage and protocol-specific test constraints; critical censorship discoveries — including GFW fully-encrypted protocol blocking and regional Chinese censorship — were first surfaced by user reports on forums and GitHub issue pages of circumvention tools, not by automated measurement infrastructure.

2026-zohaib-extended §1 measurement-platformfully-encrypted-detect

CensorAlert generates text embeddings (OpenAI text-embedding-3-small) from each item's summary, title, and tags to cluster semantically similar reports within configurable time windows; near-duplicates such as reposts, copied headlines, and translations are collapsed into a single canonical post that preserves all original source URLs and metadata.

2026-zohaib-extended §2 (AI-based Scoring) measurement-platform

CensorAlert's LLM agent scores each ingested item 0–5 on five independent dimensions — credibility, novelty, impact, timeliness, and verifiability — then computes a normalized significance score (0–10) from these components; items are processed every two hours using OpenAI GPT-5 Thinking (hosted on Azure) constrained to return structured JSON output.

2026-zohaib-extended §2 (AI-based Scoring) measurement-platform