Abstract
Despite being one of the most common approaches in unsupervised data analysis, a very small literature exists in applying formal methods to address data mining problems. This paper applies an abstract representation of a hierarchical categorical clustering algorithm (CCTree) to solve the problem of privacy-aware data clustering in distributed agents. The proposed methodology is based on rewriting systems, and automatically generates a global structure of the clusters. We prove that the proposed approach improves the time complexity. Moreover a metric is provided to measure the privacy gain after revealing the CCTree result. Furthermore, we discuss under what condition the CCTree clustering in distributed framework produces the comparable result to the centralized one.
This research has been partially supported by the EU Funded Projects H2020 C3IISP, GA #700294, H2020 NeCS, GA #675320, EIT Digital MCloudDaaS and partially by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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Sheikhalishahi, M., Mejri, M., Tawbi, N., Martinelli, F. (2017). Privacy-Aware Data Sharing in a Tree-Based Categorical Clustering Algorithm. In: Cuppens, F., Wang, L., Cuppens-Boulahia, N., Tawbi, N., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2016. Lecture Notes in Computer Science(), vol 10128. Springer, Cham. https://doi.org/10.1007/978-3-319-51966-1_11
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