BACKGROUND OF THE INVENTION

1. The Field of the Invention

The present invention relates to a method for detecting web tracking services, in particular for detecting first and third-party tracking services.

2. The Relevant Technology

Tracking services business is based on the collection of information regarding users. When browsing, users are consistently tracked by parties whose business builds on the value of collected data. A tracking service is usually a satellite service linked to a web portal. When a user visits the portal, the tracking service persuades the user's browser to download an artificial information, for example a pixel of the page or an advertising banner.

When the user generates an HTTP request towards the tracking service, this latter records the visit in its own database, sometimes together with all the information reachable at HTTP level (for example, the IP address linked to the user's device, the device and client type, etc.) and at system level (for example, the CPU load, the quantity of memory used, etc.).

The last years witnessed the silent growth of these web tracking services: collecting information about users' online activity is one of the most profitable activity in the Internet. There are hundreds of companies which base their whole business on it. A countless number of web tracking technologies are in use and tens of business models have been developed around web tracking. This phenomenon is ubiquitous, with both major and mostly unknown players taking part in it.

Due to the fact that tracking services are usually linked to many portals, the same user can be monitored and tracked by various sites.

Once collected, the tracking service uses the data for commercial purposes, for example for creating user profiles for marketing or for elaborating customized commercial advertisements, or data are sold to analysts and advertising agencies.

Despite the fact that tracking services are quite common and play an important role in the web economy, users are almost completely unaware of them and of the fact that someone can make money out of the data that they leave during their on-line activity.

The privacy implications are serious. Consumers and corporates do worry about the information they unknowingly expose to the outside world, and they claim for mechanisms to curb this leakage.

The use of web tracking practice causes leakage of information that users and companies would like to keep private: from sexual or religious preferences, to simple browsing histories. Many surveys have demonstrated that consumers and corporates would like to take control on the information they expose to web trackers. Governments and policymakers have taken steps to intervene and advocated new technical approaches to enhance consumer choice about web tracking.

Hence, there is a large ongoing effort to build technical countermeasures against web tracking. For instance, big players have proposed their own anti-tracking feature. Many plugins have been introduced to block interactions among the browser and tracking services. So far, the research community has focused on disclosing and quantifying the vastness of the problem, but only a few solutions have been proposed to curb this phenomenon.

First countermeasures to web tracking are based on blacklisting of tracking services and contents. As web tracking has raised many concerns about how it may affect users' privacy, many tracker-blocking applications, mostly being browser plugins, are available. They basically filter HTTP requests generated to tracking services. These applications rely on blacklists built offline to prevent the browser to generate HTTP requests to web trackers. However, how these blacklists are generated is impossible to know, and they are difficult to maintain over time.

In a different approach, a plugin for browser exits which analyzes how the cookies are manipulated and from which services. In a nutshell, this approach labels as trackers the owners of the pieces of code handling cookies and Adobe Flash plugins containing user identifiers. Such approach is based on the analysis of the Javascript or Flash code contained in the web pages.

However, simple actions such as blocking cookies are easily bypassed by web tracking services. For instance, a common workaround is to embed user identifiers in URL queries contained in HTTP requests.

Another approach is based on graph analysis techniques: the structure of the web pages is modelled as a graph and machine learning techniques are used to analyze the structure of webpage code and discover portion of code suspected of collecting user information thus identifying web trackers. In this case again, the detection of tracking services is based on the analysis of web pages themselves.

The main disadvantage of these methods is that they require a supervision from an analyst, which studies the web pages and use predefined classification models, which are static and must be changed time by time.

Hence, we need a method for detecting services running some tracking activity. The method must be is easy to use and automatically detect these services without the need of the assistance of an operator, thus generating curated blacklists that may be employed by any browser to block the web tracking services users encounter.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention relate to a method for detecting tracking services which overcomes the disadvantages of the prior art.

In one embodiment, the method of the present invention for detecting web tracking services during browsing activity performed by clients having associated client identifiers comprises the steps of extracting key-value pairs contained into navigation data; looking for (4) one-to-one correspondence between said client identifiers and the values contained in said keys; selecting the keys for which at least a client-value one-to-one correspondence for at least a predetermined number of clients is observed, said keys identifying the associated services as services performing tracking activities.

In another embodiment, the one-to-one correspondence is observed, for each client, across different and progressive uses of the same navigation data.

In another embodiment, the navigation data are HTTP or HTTPS GET requests or data transmitted via POST requests or data embedded in cookies.

In another embodiment, the first-party tracking services are detected.

In another embodiment, third-party tracking services are detected.

In another embodiment, combination of keys whose values exhibit one-to-one correspondence with a client are detected.

In another embodiment, the predetermined number of clients is determined so as neither to misclassify keys that contain other kind of information nor to cut out legit positive keys associated to a large set of third-party objects that may not be always present.

