Data Security in Website Tracking

June 17, 2020

Tracking of our browsing behavior is part of the daily routine of Internet use. Companies use it to adapt ads to the personal needs of potential clients or to measure their range. Many providers of tracking services advertise secure data protection by generalizing datasets and anonymizing data in this way. Computer scientists of Karlsruhe Institute of Technology (KIT) and Technische Universität Dresden (TUD) have now studied how secure this method is and reported their findings in a scientific paper for the IEEE Security and Privacy Conference.

Tracking services collect large amounts of data of Internet users. These data include the websites accessed, but also information on the end devices used, the time of access (timestamp) or location information. "As these data are highly sensitive and have a high personal reference, many companies use generalization to apparently anonymize them and to bypass data security regulations," says Professor Thorsten Strufe, Head of the "Practical IT Security" Research Group of KIT. By means of generalization, the level of detailing of the information is reduced, such that an identification of individuals is supposed to be impossible. For example, location information is restricted to the region, the time of access is limited to the day, or the IP address is shortened by some figures. Strufe, together with his team and colleagues of TUD, have now studied whether this method really allows no conclusions to be drawn with respect to the individual.

With the help of a large volume of metadata of German websites with 66 million users and over 2 billion page views, the computer scientists succeeded in not only drawing conclusions with respect to the websites accessed, but also with respect to the chains of page views, the so-called click traces. The data were made available by INFOnline GmbH, an institution measuring the data range in Germany.

The Course of Page Views Is of High Importance

"To test the effectiveness of generalization, we analyzed two application scenarios," Strufe says. "First, we checked all click traces for uniqueness. If a click trace, that is the course of several successive page views, can be distinguished clearly from others, it is no longer anonymous." It was found that information on the website accessed and the browser used has to be removed completely from the data to prevent conclusions to be drawn with respect to persons. "The data will only become anonymous, when the sequences of single clicks are shortened, which means that they are stored without any context, or when all information, except for the timestamp, is removed," Strufe says. "Even if the domain, the allocation to a subject, such as politics or sports, and the time are stored on a daily basis only, 35 to 40 percent of the data can be assigned to individuals." For this scenario, the researchers found that generalization does not correspond to the definition of anonymity.

A Few Observations Are Sufficient to Identify User Profiles

In addition, the researchers checked whether even subsets of a click trace allow conclusions to be drawn with respect to individuals. "We linked the generalized information from the database to other observations, such as links shared on social media or in chats. If, for example, the time is generalized precisely to the minute, one observation is sufficient to clearly assign 20 percent of the click traces to a person," says Clemens Deusser, doctoral researcher of Strufe's team, who was largely involved in the study. "Another two observations increase the success to more than 50 percent. Then, it is easily obvious from the database which other websites were accessed by the person and which contents were viewed." Even if the timestamp is stored with the precision of a day, only five additional observations are needed to identify the person.

"Our results suggest that simple generalization is not suited for effectively anonymizing web tracking data. The data remain sharp to the person and anonymization is ineffective. To reach effective data protection, methods extending far beyond have to be applied, such as noise by the random insertion of minor misobservations into the data," Strufe recommends.
The team of Strufe presented its findings at the "IEEE Security and Privacy Conference" from May 18 - 20, 2020. Since 1980, internationally renowned top researchers have met at this leading international conference on IT security.

Explanation of the results in the video:

Press contact:
Sandra Wiebe, Redakteurin/Pressereferentin, Tel.: +49 721 608-21172,

Being "The Research University in the Helmholtz Association", KIT creates and imparts knowledge for the society and the environment. It is the objective to make significant contributions to the global challenges in the fields of energy, mobility, and information. For this, about 9,300 employees cooperate in a broad range of disciplines in natural sciences, engineering sciences, economics, and the humanities and social sciences. KIT prepares its 24,400 students for responsible tasks in society, industry, and science by offering research-based study programs. Innovation efforts at KIT build a bridge between important scientific findings and their application for the benefit of society, economic prosperity, and the preservation of our natural basis of life. KIT is one of the German universities of excellence.

Karlsruher Institut für Technologie (KIT)

Related Data Articles from Brightsurf:

Keep the data coming
A continuous data supply ensures data-intensive simulations can run at maximum speed.

Astronomers are bulging with data
For the first time, over 250 million stars in our galaxy's bulge have been surveyed in near-ultraviolet, optical, and near-infrared light, opening the door for astronomers to reexamine key questions about the Milky Way's formation and history.

Novel method for measuring spatial dependencies turns less data into more data
Researcher makes 'little data' act big through, the application of mathematical techniques normally used for time-series, to spatial processes.

Ups and downs in COVID-19 data may be caused by data reporting practices
As data accumulates on COVID-19 cases and deaths, researchers have observed patterns of peaks and valleys that repeat on a near-weekly basis.

Data centers use less energy than you think
Using the most detailed model to date of global data center energy use, researchers found that massive efficiency gains by data centers have kept energy use roughly flat over the past decade.

Storing data in music
Researchers at ETH Zurich have developed a technique for embedding data in music and transmitting it to a smartphone.

Life data economics: calling for new models to assess the value of human data
After the collapse of the blockchain bubble a number of research organisations are developing platforms to enable individual ownership of life data and establish the data valuation and pricing models.

Geoscience data group urges all scientific disciplines to make data open and accessible
Institutions, science funders, data repositories, publishers, researchers and scientific societies from all scientific disciplines must work together to ensure all scientific data are easy to find, access and use, according to a new commentary in Nature by members of the Enabling FAIR Data Steering Committee.

Democratizing data science
MIT researchers are hoping to advance the democratization of data science with a new tool for nonstatisticians that automatically generates models for analyzing raw data.

Getting the most out of atmospheric data analysis
An international team including researchers from Kanazawa University used a new approach to analyze an atmospheric data set spanning 18 years for the investigation of new-particle formation.

Read More: Data News and Data Current Events is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to