Privacy Preserving Spatio-Temporal Clustering on Horizontally Partitioned Data

  • Ali İnan
  • Yücel Saygın
Conference paper

DOI: 10.1007/11823728_44

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4081)
Cite this paper as:
İnan A., Saygın Y. (2006) Privacy Preserving Spatio-Temporal Clustering on Horizontally Partitioned Data. In: Tjoa A.M., Trujillo J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2006. Lecture Notes in Computer Science, vol 4081. Springer, Berlin, Heidelberg

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ali İnan
    • 1
  • Yücel Saygın
    • 1
  1. 1.Faculty of Engineering and Natural SciencesSabancı UniversityIstanbulTurkey

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