How do EFF Cover Your Tracks and AmIUnique measure uniqueness and what do their scores mean?

1. What the tools actually collect and test

Both services probe the same observable surface: browser and operating system version strings, installed plugins and fonts, canvas/WebGL outputs, screen size, language, and other JavaScript- or HTTP-exposed attributes that together form a browser “fingerprint” ^{[1] [4] [2]}. The EFF project explicitly simulates how trackers see a browser and also tests which third‑party trackers and tracker-blocking protections are able to load, while AmIUnique focuses on the composition and distinctiveness of the fingerprint attributes themselves ^{[3] [5] [2]}.

2. How uniqueness is measured — comparison to a reference population

Uniqueness scores are produced by comparing the collected attribute vector for the visitor to a database of previously observed fingerprints; if that exact or very similar combination is rare in the dataset, the fingerprint is scored as more unique ^{[1] [6]}. EFF’s Panopticlick lineage and Cover Your Tracks methodology explain that the project aggregates many users’ configurations and then generates a metric that expresses how identifiable the tested browser is among that pool ^{[4] [1]}. AmIUnique reports similarity per attribute and calculates entropy-like measures to show how much each signal contributes to identifiability ^[2].

3. What the scores mean in plain terms

A high uniqueness score means the browser’s observable combination of attributes is uncommon in the tool’s dataset, which makes it easier for passive trackers to link requests from that browser across different sites without cookies; a low uniqueness score means the browser “blends in” with many others and is harder to single out ^{[3] [2]}. EFF’s tool also reports whether tracker-blocking extensions or settings prevented simulated trackers from loading, so a “protected” result can still coexist with a unique fingerprint if the observable attributes remain distinctive ^{[3] [5] [6]}.

4. Key differences between Cover Your Tracks and AmIUnique

Cover Your Tracks is an advocacy-focused, research-driven tester from EFF that both mimics trackers and evaluates the effectiveness of privacy add‑ons and browser settings in blocking those trackers, building on the older Panopticlick project ^{[4] [3]}. AmIUnique is framed more as a research and verification service that breaks down similarity scores by attribute and measures how well camouflage or anti-detect techniques change fingerprint entropy, making it popular with developers testing mitigation approaches ^{[2] [5]}.

5. Important caveats and limits of interpretation

These tools are diagnostic, not protective: running a test does not stop tracking, and results depend on the representativeness of each tool’s database — sample bias and the set of attributes probed affect scores, so a “unique” result means uniqueness relative to the project’s dataset and methods, not an absolute legal or forensic certainty ^{[1] [7] [2]}. Also, a browser can score as unique even when tracker blockers are active; Lifehacker’s reporting found users could still get high uniqueness scores despite privacy extensions or Tor settings, illustrating the gap between blocking trackers and minimizing fingerprint distinctiveness ^{[6] [3]}.

6. How to use scores responsibly

Treat scores as actionable intelligence: a high uniqueness score should prompt steps to reduce exposed signals (use browsers that standardize surfaces, disable unnecessary plugins, prefer privacy-first defaults) and to enable tracker-blockers that Cover Your Tracks will show as effective, while a low uniqueness/similarity score indicates better blending but not immunity to other tracking vectors like authenticated accounts or IP correlations ^{[3] [5] [2]}. Both tools are best used together: AmIUnique for attribute-level diagnosis and entropy insight, and Cover Your Tracks to validate whether tracker-blocking settings actually stop third‑party trackers from loading ^{[2] [3]}.