Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mix Zones: User Privacy in Location-aware Services
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Protecting Location Privacy Through Path Confusion
SECURECOMM '05 Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks
Privacy Preserving Clustering on Horizontally Partitioned Data
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
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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.