Abstract
Several privacy measures have been proposed in the privacy-preserving data mining literature. However, privacy measures either assume centralized data source or that no insider is going to try to infer some information. This paper presents distributed privacy measures that take into account collusion attacks and point level breaches for distributed data clustering. An analysis of representative distributed data clustering algorithms show that collusion is an important source of privacy issues and that the analyzed algorithms exhibit different vulnerabilities to collusion groups.
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Notes
- 1.
This notion comes from the well-known idea in computer security that defines the security level of a system as the level of its weakest link.
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Acknowledgment
This work was partly supported by the EU-funded project TOREADOR (contract n. H2020-688797)
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da Silva, J.C., Klusch, M., Lodi, S. (2016). Privacy-Awareness of Distributed Data Clustering Algorithms Revisited. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_23
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