Abstract
Privacy is an ever-growing concern in our society and is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving human personal sensitive information, like data referring to individual mobility. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. This is especially true when we work on mobility data, that are characterized by the fact that there is no longer a clear distinction between quasi-identifiers and sensitive attributes. Therefore, protecting privacy in this context is a significant challenge. As a result, privacy preservation simply cannot be accomplished by de-identification alone. In this chapter, we propose the Privacy by Design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start. We show three applications of the Privacy by Design principle on mobility data analytics. First we present a framework based on a data-driven spatial generalization, which is suitable for the privacy-aware publication of movement data in order to enable clustering analysis. Second, we present a method for sanitizing semantic trajectories, using a generalization of visited places based on a taxonomy of locations. The private data then may be used for extracting frequent sequential patterns. Lastly, we show how to apply the idea of Privacy by Design in a distributed setting in which movement data from individual vehicles is made private through differential privacy manipulations and then is collected, aggregated and analyzed by a centralized station.
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This definition of de-identified data is compliant with the General Data Protection Regulation (GDPR) [18], especially referring to Recital 26. Indeed, with the de-identification process we are going to transform identified persons in identifiable persons.
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Pratesi, F., Monreale, A., Pedreschi, D. (2018). Privacy by Design for Mobility Data Analytics. In: Gkoulalas-Divanis, A., Bettini, C. (eds) Handbook of Mobile Data Privacy . Springer, Cham. https://doi.org/10.1007/978-3-319-98161-1_10
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