Stripping names feels like anonymization, but combinations of leftover attributes can still single out individuals — apparent anonymity is not real anonymity.

This idea, associated with Harvard's Latanya Sweeney, is the thesis the whole field is built on. Sweeney famously showed that 87% of Americans could be uniquely identified by just three "harmless" attributes: 5-digit ZIP code, gender, and date of birth. None of those is a name, yet together they fingerprint almost everyone.

The lesson: anonymization is not about deleting the obvious identifiers. It's about reasoning over every attribute and every way they can be combined — within your dataset and against outside data — to re-pin a record to a person.

Tip: "Anonymous-looking" is a statement about how the data feels; "anonymous" is a statement about what an attacker can do. Only the second one counts.

Go deeper:

• k-Anonymity: A Model for Protecting Privacy (Sweeney, 2002) — the primary paper behind the 87%-from-ZIP-gender-DOB result.
• Data re-identification (Wikipedia) — surveys how "anonymous" releases keep getting reversed.

A data broker holding precise location histories from thousands of apps was breached, exposing where millions of people had physically been.

Gravy Analytics (a subsidiary of Unacast) aggregated location data harvested from everyday apps — Tinder, Grindr, AccuWeather, Candy Crush, Flightradar24 and thousands more. When its cloud storage was accessed without authorization, the leaked traces could link individuals to sensitive places: places of worship, medical clinics, political events, and private homes.

Two takeaways:

• Location is uniquely revealing. Where you go discloses your religion, health, relationships, and politics — even if no "name" field is attached.
• The data broker layer multiplies risk. You may trust one app, but its SDK can quietly ship your coordinates to brokers who pool everyone's data into one juicy target.

Tip: Four location points are enough to uniquely identify ~95% of people (de Montjoye et al., 2013). Location is essentially a built-in fingerprint.

Go deeper:

• Unique in the Crowd: privacy bounds of human mobility (de Montjoye, 2013) — four location points uniquely identify ~95% of people.
• Big broker is tracking you! (ACSAC '25) — privacy assessment of large-scale broker location traces.