This explains how device fingerprinting has taken over where third‑party cookies left off: advertisers and other data brokers combine small, consistent bits of information from browsers and networks to build stable identifiers that follow people across sites and devices. It traces industry moves - privacy promises, stalled cookie deprecation, and the push to bake fingerprinting into ad products - and argues the product being sold is pattern-based intelligence about behavior, not individual profiles for users’ benefit. The piece warns that fingerprints don’t need to be globally unique, only unique within a sample, and that aggregated signals can reveal far more than a single piece of personal data.
The technical core shows how fingerprints are created and why they work: network headers and IPs provide fragile signals, while in‑browser measurements give stable, high‑entropy traits. Specific techniques demonstrated include OfflineAudioContext audio fingerprints, canvas and WebGL pixel rendering and text drawing, Intl locale/timezone/number formats, and navigator hardware fields (CPU cores, memory), plus font and GPU quirks. Entropy and surprisal are used to quantify how traits narrow candidate pools. Practical defenses require engine‑level changes: Tor, Brave and similar browsers offer the strongest protections, extensions often increase uniqueness, and simple VPNs or clearing cookies do not stop advanced fingerprinting.
Summary generated by AI from the linked article. hn.today is not affiliated with Hacker News or Y Combinator.
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