Anonymization of vehicle position data – potentials, obstacles & limitations

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Jun-Gyu Kim
data scientist

With the General Data Protection Regulation (GDPR), the EU ensured the data anonymity, in particular the anonymity on the Internet. If a word of the year or even decade is sought, then "data anonymity" is certainly a hot contender, as it not only led to the foundation of numerous discussions in our society, but also poses new challenges to companies. It is agreed that personal data is the fuel of marketing measures and can make the difference in today's competitive market. Therefore, the  companies had to come up with a solution quickly, and one of them is the so-called anonymization of data.

anonymisierung von fahrzeugpositionsdaten | clustering | diconium

Anonymization of vehicle position data – potentials, obstacles & limitations

This blog series shows briefly in a high-level presentation the potentials and limits of data anonymization with vehicle position data. In this first blog entry, we use concrete examples to illustrate the business impact of anonymized data across different sectors, and in the second blog entry we show the obstacles that inevitably confront anonymization.

But what exactly is anonymization? 

In essence, it describes a process where personal data is transformed in such a way that it can not be traced back to individual persons. Is the anonymization process reversible? As in the world of viruses and anti-virus software, the anonymization concept is also a kind of robber and gendarme game. It's more about not making the hacker too easy. A popular and simple method is the so-called clustering, where several data points are summarized to data clouds and filled with other useful information.

anonymization of vehicle position data  | clustering | diconium An example of how data points are aggregated into clusters. Only the number in the cluster gives an indication of how many data points are in this data cloud.

Thus, anonymized data can still provide information for various use cases, which can increase the value of business value for a company.

Consider the following situation:
Showplace: Germany
Actors: The favorite toy of the Germans, the car.
Scenario: In today's digital world, almost every car driver possesses an item that transmits its GPS position while driving, be it the navigation device or the car itself. Even the smartphone, if not one's own then that of the passengers, is one of them.


The GPS signals thus sent throughout Germany can now be used with cluster methods in so-called heatmap analyzes. The knowledge gained is then passed on to various applications - ideally fully automatically and in real time. A very slowly moving cluster on the highway can thus be recognized as a traffic jam. Information that can provide a significant advantage to a navigation system and improve the customer satisfaction.

Other quite interesting use cases would be the following:
1. Where should I place the charging stations for the electric cars, so that the optimal utilization is achieved? In the e-mobility competition, this can make the difference between a satisfied or dissatisfied customer.
2. On which roads or intersections should I place my advertising for drivers? With the maximum impact of advertising the optimal visibility of the products is guaranteed.
3. Which routes do the drivers use at what time, so that I can make a precise forecast of the city traffic? For example, the city council may Improve the traffic situation and also the satisfaction of the citizens with optimized traffic lights.
Vehicle data can thus be used across industries to meet the needs of people. Of course, as always, this is related to how politics, businesses and individual users handle the data. But this is another story.

diconium is a specialist in the fields of data and artificial intelligence. Whether in the areas of search, social and content, personalization and analytics or data science – our expertise helps you to collect the right data at the right time, to forecast services and offers and thus to make data-driven decisions. We look forward to supporting you according to your individual needs.

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Jun-Gyu Kim
data scientist