Direct answer
Concepts and operation are useful against online tracking when you translate abstract tracking behavior into practical expectations: what gets collected, what triggers requests, and how changing network paths or browser behavior affects those requests. Their limits are that no single tool reliably blocks all tracking, and outcomes vary with your network, device, location, time, and the services you visit.
What the idea means (definitions and operating conditions)
In this context, “concepts” means understanding the building blocks of tracking: identifiers (like cookies), tracking pixels, device/browser attributes, and the way requests connect to third parties (ad networks, analytics, or embedded services). “Operation” means the practical mechanisms that change what the website can observe—most commonly by altering how your traffic is routed and how identifiable browser data is handled.
This helps when your goal is risk reduction and fewer trackable signals, not absolute invisibility. It’s also most effective when you align your approach with the specific tracking channels you’re trying to reduce (for example, third-party embedded content versus first-party sessions).
How it works in practice (simple model)
A useful mental model is: websites observe behaviors through signals; signals come from requests and stored identifiers; and operation can change either the path of requests or the presence/consistency of identifiers.
For example, if a service relies heavily on network-visible identifiers and third-party requests, changing network routing and reducing persistent identifiers can reduce correlation. If the service can still use your account activity or consistent in-session patterns, tracking may continue even when some signals are reduced.
Limitations and exceptions
A key limit is that “tracking” is broader than one signal. Even if one channel is reduced, others can remain (logged-in activity, fingerprintable settings, behavioral patterns, or server-side profiling). Performance and availability can vary by network, device, location, provider, and time.
Also, be cautious with claims that suggest guaranteed anonymity, safety, or access—those are not realistic outcomes to rely on. Treat results as probabilistic and validate them for your actual use cases.