To test how trackable your browser is, run EFF’s Cover Your Tracks in the browser you want to assess. Its report separately evaluates whether test trackers were blocked and how distinctive your browser fingerprint appears in its visitor sample. Neither result is a verdict on your identity or proof that all tracking is stopped.
How to test your browser fingerprint
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Open Cover Your Tracks in the browser and profile you want to check, then run its test. A result applies to that browser configuration, not automatically to your other browsers or profiles.
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Record the browser and version, operating system, date, test mode, and the report’s exact wording or displayed estimate. Results may differ later if software, settings, or the comparison sample changes.
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Read the tracker-blocking and fingerprint-uniqueness parts separately. The test simulates tracker requests and observes whether protections stop them, while also comparing browser fingerprints with other visitors’ results. EFF describes these as distinct parts of the project in its methodology.
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If you want to see whether a particular change affects the report, change one setting at a time and run the test again. Treat each result as this tool’s report for your browser at that time, not as a controlled benchmark or a universal measure.
What a browser fingerprint is
A browser fingerprint is a combination of details a website can observe about your browser, device, configuration, or behavior. Possible signals include time zone and operating-system settings, graphics rendering, available fonts, hardware details, and other settings. A fingerprint can help distinguish a browser when its combination is unusual, and can help link visits when it remains consistent. EFF explains fingerprinting in its overview.
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Cookies store identifiers on your device and can be deleted. Fingerprinting instead can draw on characteristics a site observes without depending on one stored cookie. It is not literally impossible to change: browser updates and privacy protections can limit or standardize exposed characteristics. But there is no single aggregate fingerprint you can reliably clear in the same simple way as deleting one cookie.
What a “unique” result means
A uniqueness result compares your tested fingerprint with the tool’s observed population. “Unique” means unusual in that comparison sample; it does not establish that a particular company collected the fingerprint, that your real name is known, or that every website uses the same technique.
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Cover Your Tracks’ live results page displays estimates based on visitors who took the test during a recent time window. The visitor count and estimates change as the sample changes. The page also cautions that “because tracking techniques are complex, subtle, and constantly evolving, Cover Your Tracks does not measure all forms of tracking and protection.” See its live results page for current output; do not treat a figure seen at another time as a stable general statistic.
A less distinctive result is not a guarantee of anonymity or protection from cookies, account logins, IP-based correlation, or tracking outside the test’s coverage. Likewise, a good tracker-blocking result only describes the tracker requests observed in this test; it does not make a separate uniqueness result unnecessary.
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A small browser fingerprint code demonstration
This example reads a few ordinary browser-visible values and renders a canvas data URL locally in the page. It is deliberately incomplete, does not transmit the values, and does not calculate a trackability or uniqueness score.
async function showBrowserSignals() {
const canvas = document.createElement("canvas");
canvas.width = 240;
canvas.height = 60;
const context = canvas.getContext("2d");
context.textBaseline = "top";
context.font = "16px Arial";
context.fillStyle = "#345";
context.fillText("Browser fingerprint demo", 4, 4);
const signals = {
language: navigator.language,
platform: navigator.platform,
timezone: Intl.DateTimeFormat().resolvedOptions().timeZone,
screen: `${screen.width}x${screen.height}x${screen.colorDepth}`,
canvasDataUrl: canvas.toDataURL()
};
console.table(signals);
return signals;
}
showBrowserSignals();To try it, paste the code into your browser’s developer console on a page you trust, or run it from a local HTML file. It illustrates that scripts can inspect browser-visible properties; it is not equivalent to Cover Your Tracks or a complete fingerprinting library. A uniqueness estimate requires comparison data from other browsers, not just values collected from your own. EFF’s test report includes headers and JavaScript-derived characteristics and describes its comparison database in the methodology. Avoid uploading collected values to an untrusted service.
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Ways to reduce browser fingerprinting
Use built-in browser protections
Prefer protections that reduce information exposed to sites and block known trackers. Mozilla says Firefox fingerprinting protections affect information such as graphics, fonts, and hardware details. Its announcement described the newer protections as initially available in Private Browsing and Enhanced Tracking Protection Strict mode; availability can change by release, so check Mozilla’s current Firefox documentation for your version.
WebKit describes measures including restricting font availability and avoiding user-agent changes for minor updates in its Tracking Prevention documentation. These are documented implementation choices, not evidence that Safari is universally or uniquely the safest browser.
Understand the limits of blockers and restrictive settings
Tracker blockers can stop known tracking scripts or domains from loading, but unknown trackers and first-party tracking can remain. Disabling JavaScript can reduce data available to many fingerprinting scripts, but it can also break or limit functionality on many sites. EFF discusses these tradeoffs in its Cover Your Tracks learning guide.
Tor Browser’s approach, as EFF describes it, includes making browser instances look more alike to reduce distinctiveness. This is a design strategy, not a promise that every tracking path is blocked. Conversely, randomly changing individual settings, spoofing one user-agent field, or piling on unusual privacy extensions may make a configuration stand out. EFF discusses the risks of custom configurations in its fingerprinting guide.
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In a 2010 study, EFF researcher Peter Eckersley analyzed a sample of 470,161 browsers and reported at least 18.1 bits of entropy in the observed fingerprint distribution. Under that study’s distribution, the paper described a best-case sharing estimate of one in 286,777. The sample was biased toward privacy-aware visitors, so those figures describe that historical study—not today’s general browser population or an individual reader’s odds. See How Unique Is Your Web Browser?.
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