Data Warehousing and Knowledge Discovery

Volume 4081 of the series Lecture Notes in Computer Science pp 459-468

Privacy Preserving Spatio-Temporal Clustering on Horizontally Partitioned Data

  • Ali İnanAffiliated withFaculty of Engineering and Natural Sciences, Sabancı University
  • , Yücel SaygınAffiliated withFaculty of Engineering and Natural Sciences, Sabancı University

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Time-stamped location information is regarded as spatio-temporal data and, by its nature, such data is highly sensitive from the perspective of privacy. In this paper, we propose a privacy preserving spatio-temporal clustering method for horizontally partitioned data which, to the best of our knowledge, was not done before. 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 and complexity analysis of our methods shows that our protocol does not introduce extra overhead when constructing dissimilarity matrix, compared to the centralized approach.