You don't need to create an account on a website for your data to be collected. Sometimes, simply solving a CAPTCHA or clicking on an embedded video on a web page is enough to trigger data exchanges with third-party services. These mechanisms may serve legitimate purposes, such as fighting bots, preventing fraud, or securing websites. But a paradox emerges: what helps protect a service can also expose users to greater personal data collection.
A data breach does not require malicious intent. Human error, a misconfiguration, or a security vulnerability may be enough to expose sometimes highly sensitive information. Even when a company acts in good faith, the data it collects and retains can pose a risk to the people concerned. This risk is all the more difficult to assess when collection takes place in the background through third-party services whose very existence users may not even be aware of.
These actors, which could be described as "invisible", operate across many websites to provide functionality, measure audience traffic, prevent fraud, or recognize certain devices. Using various identification techniques, including fingerprinting — the digital fingerprint of a browser and device —, they can distinguish a device and associate multiple interactions with the same profile, without necessarily requiring users to create an account with them.
Of course, these services do not always know your name or email address directly. They may rely on technical identifiers or data described as anonymous or pseudonymized. However, an IP address, a digital fingerprint, or other technical characteristics can help distinguish a device and, when combined with additional information, make it easier to identify a person.
The risk therefore extends beyond data that directly identifies someone. Information that appears harmless in isolation can become far more revealing when combined with other data. If several organizations experience data breaches, comparisons between their databases may, under certain circumstances, make it possible to link technical identifiers to personal information. Advances in analytical tools, particularly those based on artificial intelligence, may facilitate some of these connections, although they do not guarantee that re-identification will always be possible.
Consider a concrete example. You visit website A, which uses a third-party service B. This service collects certain technical information about your device, including a digital fingerprint. Later, you create an account on website C, which collects your email address and also uses techniques to recognize your device. Both service B and website C subsequently suffer data breaches.
If the exposed information is sufficiently compatible and the digital fingerprint is stable and distinctive enough, matching the two databases may make it possible to associate the technical data collected by B with the email address held by C. Information collected in a context where you never registered with service B could then be linked to your identity.
This example illustrates a fundamental risk of indirect data collection: the more organizations hold fragments of information about you, the greater the opportunities for data matching and re-identification may become, even when each organization, considered individually, appears to possess only limited information.