Privacy Preserving Spatio-Temporal Clustering on Horizontally Partitioned Data

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.

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