What technical methods does DuckDuckGo Tracker Radar use to detect and classify trackers?

1. Automated crawling: a Puppeteer-driven, modular collector that simulates browsing

Tracker Radar’s first technical layer is an automated, multithreaded crawler built on Puppeteer that visits top websites to record third‑party network activity and in-page behavior; the collector is modular so different “collectors” can extend a BaseCollector to customize what’s captured and how ^[1]. That crawling approach lets DuckDuckGo gather raw traces of network requests, frames, cookies set in third-party contexts, and other observable signals across many sites continuously rather than relying only on static blocklists or vendor disclosures ^{[3] [6]}.

2. Request-level signals: third‑party context, cookies, and resource usage

From the crawl output, the detector inspects whether domains are loaded in a first‑ or third‑party context, whether they set cookies, and what kinds of resources they request (scripts, images, beacons, iframes) to flag likely trackers; documentation and marketing materials emphasize “usage of resources in a third‑party context” and “cookie-setting behavior” as core detection signals ^{[5] [4]}. The Collector intentionally records the network-level footprint so the downstream detector can quantify prevalence and behavioral patterns across sites, forming the basis for classification and metadata ^{[1] [2]}.

3. Fingerprinting and browser API analysis: spotting cross‑site identification techniques

Tracker Radar’s tooling looks for usage of browser APIs and patterns associated with fingerprinting — for example, access to canvas, audio, or other fingerprinting‑prone APIs — and scores the “likelihood” that these APIs would be used to identify users, which becomes part of the tracker metadata ^[5]. The detector codebase includes specific processing steps (e.g., tracking-params and process-crawl scripts referenced in package.json) to extract and tabulate these signals from the raw crawl data for later labeling ^{[7] [2]}.

4. CNAME cloaking and treating cloaked hosts as third parties

An explicit technical focus is detecting and neutralizing CNAME cloaking, where third‑party trackers hide behind subdomains that look first‑party; Tracker Radar’s outputs are used to treat those cloaked domains as proper third parties so protective measures can apply ^[4]. DuckDuckGo’s help pages and the dataset’s metadata enable clients (browsers or extensions) to translate detector findings into protections that block or rewrite calls to these resolved third-party endpoints ^{[4] [3]}.

5. Detector pipeline, metadata model and open dataset practices

The detector is a separate open-source component that ingests the collector’s crawl dumps and runs a build pipeline to produce the Tracker Radar dataset: scripts process crawls, extract tracking parameters, compute prevalence and behavioral flags, and emit a dataset of third‑party domains enriched with metadata and labels ^{[2] [7]}. The resulting repository is deliberately not just a blocklist but a rich data model intended for research, blocklist generation, and integrations (e.g., Vivaldi, DuckDuckGo extensions), and it’s maintained as a continuously updated public dataset ^{[3] [5]}.

6. Uses, limitations, and the source’s agenda

DuckDuckGo positions Tracker Radar as an automated, empirically driven counter to brittle blocklists and a foundation for privacy protections, but the methodology depends on what the crawler can observe: scripted or server‑side evasion, conditional trackers, or non‑observable linking tactics may be missed or require heuristic interpretation ^{[6] [1]}. As an open-source project maintained by DuckDuckGo, the dataset and tooling carry the company’s privacy priorities and product incentives—useful for transparency and community auditability, but not neutral academic ground truth without complementary methods or external validation ^{[3] [6]}.