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Leveraging location information in location-based services leads to improving service utility through geocontextualization. However, this raises privacy concerns as new knowledge can be inferred from location records, such as user's home and work places, or personal habits. Although Location Privacy Protection Mechanisms (LPPMs) provide a means to tackle this problem, they often require manual configuration...
With the increasing amount of mobility data being collected on a daily basis by location-based services (LBSs) comes a new range of threats for users, related to the over-sharing of their location information. To deal with this issue, several location privacy protection mechanisms (LPPMs) have been proposed in the past years. However, each of these mechanisms comes with different configuration parameters...
An increasing amount of mobility data is being collected every day by different means, such as mobile applications or crowd-sensing campaigns. This data is sometimes published after the application of simple anonymization techniques (e.g., putting an identifier instead of the users' names), which might lead to severe threats to the privacy of the participating users. Literature contains more sophisticated...
An increasing amount of mobility data is being collected every day by different means, e.g., By mobile phone operators. This data is sometimes published after the application of simple anonymization techniques, which might lead to severe privacy threats. We propose in this paper a new solution whose novelty is two-fold. Firstly, we introduce an algorithm designed to hide places where a user stops...
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