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.
This work was funded by the Information Society Technologies programme of the European Commission, Future and Emerging Technologies under IST-014915 GeoPKDD project.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chen, L., Özsu, M.T., Oria, V.: Robust and Fast Similarity Search for Moving Object Trajectories. In: Proc. of the 2005 ACM SIGMOD, pp. 491–502 (2005)
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering Similar Multidimensional Trajectories. In: Proc. of the 18th ICDE, pp. 673–684 (2002)
Yi, B.-K., Jagadish, H.V., Faloutsos, C.: Efficient Retrieval of Similar Time Sequences Under Time Warping. In: Proc. of the 14th ICDE, pp. 201–208 (1998)
Chen, L., Ng, R.: On the Marriage of Edit Distance and Lp-Norms. In: Proc. of the 2004 VLDB, pp. 792–803 (2004)
Oliveira, S.R.M., Zaiane, O.R.: Privacy Preserving Clustering by Object Similarity-Based Representation. In: Proc. of the 2004 ICDM Workshop on Privacy and Security Aspects of Data Mining, pp. 40–46 (2004)
Inan, A., Saygin, Y., Savas, E., Hintoglu, A.A., Levi, A.: Privacy Preserving Clustering on Horizontally Partitioned Data. In: Proc. of the 22nd ICDE Workshop on Privacy Data Management (2006)
Agrawal, R., Srikant, R.: Privacy Preserving Data Mining. In: Proc. of the 2000 ACM SIGMOD, pp. 439–450 (2000)
Jha, S., Kruger, L., Mc Daniel, P.: Privacy Preserving Clustering. In: Proc. of the 10th European Symposium on Research in Computer Security, pp. 397–417 (2005)
Merugu, S., Ghosh, J.: Privacy Preserving Distributed Clustering using Generative Models. In: Proc. of the 3rd ICDM, pp. 211–218 (2003)
Vaidya, J., Clifton, C.: Privacy Preserving K-Means Clustering over Vertically Partitioned Data. In: Proc. of the 9th ACM SIGKDD, pp. 206–215 (2003)
Hoh, B., Gruteser, M.: Protecting Location Privacy through Path Confusion. In: Proc. of the 2005 SecureComm (2005)
Beresford, A.R., Stajano, F.: Mix Zones: User Privacy in Location-Aware Services. In: Proc. of PerCom Workshops, pp. 127–131 (2004)
Beresford, A.R.: Location Privacy in Ubiquitous Computing. Ph.D. Dissertation, University of Cambridge (2004)
Saygin, Y., Verykios, V.S., Clifton, C.: Using Unknowns to Prevent Discovery of Association Rules. SIGMOD Record 30(4), 45–54 (2001)
Diffie, W., Hellman, M.E.: New Directions in Cryptography. IEEE Transactions on Information Theory IT-200, 644–654 (1976)
The R-Tree Portal (March 28, 2006), http://isl.cs.unipi.gr/db/projects/rtreeportal/trajectories.html
ppSTClusteringOnHP.zip [3510K] (March 28, 2006), http://students.sabanciuniv.edu/~inanali/ppSTClusteringOnHP.zip
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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. https://doi.org/10.1007/11823728_44
Download citation
DOI: https://doi.org/10.1007/11823728_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37736-8
Online ISBN: 978-3-540-37737-5
eBook Packages: Computer ScienceComputer Science (R0)