E.g., the guide page ends with
> The honest bottom line. Perfect anonymity [...]
We figured it out just fine.
Seems more plausible that anti-fingerprinting is throwing it off?
I know this computer has more RAM than reported and it's definitely not on a 4G network either.
In a private Safari window, it's not even stable across reloads.
GlassBox runs ~31 probes (canvas, WebGL/WebGPU, audio, fonts, the WASM feature set, math/engine quirks, WebRTC IP, timezone/locale, the permission and API matrices, an incognito heuristic, cross-site login-state, and so on) and shows the raw values plus an estimate of how identifiable you are.
A few deliberate choices:
- One static HTML file, no dependencies, no build step. Everything runs client-side and nothing is sent, with one opt-out exception: IP geolocation, which calls a public API. I didn't want a privacy tool that phones home.
- The "identifiability" number is an honest model, not a measurement. It sums published per-signal entropy (Panopticlick / AmIUnique / Cover Your Tracks), discounts signals your browser masks, and caps at the ~33 bits needed to single out one person on Earth. A no-server tool can't compute true rarity against a live population, so I label it an estimate instead of pretending. For real population numbers, Cover Your Tracks and AmIUnique have the datasets.
- There's a companion guide on lowering your fingerprint, with the caveat that uniqueness isn't privacy: blending into a big crowd (Tor at its default size) beats a bespoke hardened setup that makes you the only one who looks like that.
Source (MIT): https://github.com/HotStartLabs/glassbox
I'd genuinely like to know which vectors I'm missing, especially from the anti-fraud / detection side.