Privacy-preserving data mining
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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
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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
Privacy preserving clustering on horizontally partitioned data
Data & Knowledge Engineering
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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.