Abstract
Space and time are two important features of data collected in ubiquitous environments. Such time-stamped location information is regarded as spatio-temporal data and, by its nature, spatio-temporal data sets, when they describe the movement behavior of individuals, are highly privacy sensitive. In this chapter, we propose a privacy preserving spatio-temporal clustering method for horizontally partitioned data. Our methods are based on building the dissimilarity matrix through a series of secure multi-party trajectory comparisons managed by a third party. Our trajectory comparison protocol complies with most trajectory comparison functions. A complexity analysis of our methods shows that our protocol does not introduce extra overhead when constructing dissimilarity matrices, compared to the centralized approach.
This work was funded by the Information Society Technologies programme of the European Commission, Future and Emerging Technologies under IST-014915 GeoPKDD project.
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Inan, A., Saygin, Y. (2010). Privacy Preserving Spatio-temporal Clustering on Horizontally Partitioned Data. In: May, M., Saitta, L. (eds) Ubiquitous Knowledge Discovery. Lecture Notes in Computer Science(), vol 6202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16392-0_11
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DOI: https://doi.org/10.1007/978-3-642-16392-0_11
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