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Qualitatively, Figure 7 shows a visually comparison of results of the privacy transformation with the original ones.
From the distributions we can notice how the privacy transformation preserves very well the distribution of the original flows, even for more restrictive values of the parameter ε.
Figure 7 Visual comparison of Flow per Link and Flow per Zone measures: the resulting overview after the privacy transformation preserves relevant information and properties.
We highlight that the mobility data published after the privacy transformation strategy, described in the following, is suitable for collective data analyses useful for extracting knowledge describing the collective mobility behavior of a population.
An privacy transformation of the trajectories consists of the following steps: 1. characteristic points are extracted from the original trajectories: starting points, ending points, points of significant turn, points of significant stop (Figure 1(b)); 2.
Since the privacy transformation operates on the entries of the frequency vectors, and hence on the flows, we present the comparison (before and after the transformation) of two measures: (1) the Flow per Link, i.e. the directed volume of traffic between two adjacent zones; (2) the Flow per Zone, i.e. the sum of the incoming and outgoing flows in a zone.
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Also considering several flows together, like those incident to a given zone (Figure 6 right)), the distributions are well preserved for all the privacy transformations.
As a consequence, this kind of data can reveal sensitive user behavior and the telecommunication operator cannot release this data to the analyst without any privacy-preserving data transformation.
Following the 1989 Children's Act, which required schools to renovate their antiquated dormitories and give children more comfort and privacy, and a gradual transformation of the boarding-school environment, it is apparently again OK for parents to send their children away.
In the process, a dramatic transformation of privacy, surveillance, our concept of war, and government took place.
The main idea to protect the privacy is employing some transformations on the original MMC problem to get an encrypted MMC problem which is sent to the cloud; and then transforming the result returned from the cloud to get the correct result to the original MMC problem.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com