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.
References
Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the 20th Symposium on Principles of Database Systems (PODS), pp. 247–255. ACM, May 2001
Bertino, E., Fovino, I., Provenza, L.: A framework for evaluating privacy preserving data mining algorithms. Data Min. Knowl. Discov. 11(2), 121–154 (2005)
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.: Tools for privacy preserving data mining. ACM SIGKDD Explor. Newsl. 4(2), 28–34 (2002)
Forman, G., Zhang, B.: Distributed data clustering can be efficient and exact. SIGKDD Explor. Newsl. 2(2), 34–38 (2000)
Goldreich, O.: Foundations of Cryptography: Basic Applications, vol. 2. Cambridge University Press, Cambridge (2004)
Jones, C., Hall, J., Hale, J.: Secure distributed database mining: principle of design. In: Advances in Distributed and Parallel Knowledge Discovery, Chap. 10, pp. 277–294. AAAI Press/MIT Press, Menlo Park (2000)
Kantarcioglu, M.: A survey of privacy-preserving methods across horizontally partitioned data. In: Aggarwal, C.C., Yu, P.S. (ed.) Privacy-Preserving Data Mining. The Kluwer International Series on Advances in Database Systems, vol. 34, pp. 313–335. Springer, New York (2008)
Klusch, M., Lodi, S., Moro, G.: Agent-based distributed data mining: the KDEC scheme. In: Klusch, M., Bergamaschi, S., Edwards, P., Petta, P. (eds.) Intelligent Information Agents. LNCS (LNAI), vol. 2586, pp. 104–122. Springer, Heidelberg (2003). doi:10.1007/3-540-36561-3_5
Lindell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. J. Priv. Confidentiality 1(1), 5 (2009)
Merugu, S., Ghosh, J.: Privacy-preserving distributed clustering using generative models. In: Proceedings of the 3rd International Conference on Data Mining (ICDM). IEEE (2003)
Merugu, S., Ghosh, J.: A privacy-sensitive approach to distributed clustering. Pattern Recogn. Lett. 26, 399–410 (2005)
Patel, S.J., Punjani, D., Jinwala, D.C.: An efficient approach for privacy preserving distributed clustering in semi-honest model using elliptic curve cryptography. Int. J. Netw. Secur. 17(3), 328–339 (2015)
Provost, F.: Distributed data mining: scaling up and beyond. In: Advances in Distributed and Parallel Knowledge Discovery, pp. 3–27. AAAI Press, Palo Alto (2000)
Shen, P., Li, C.: Distributed information theoretic clustering. IEEE Trans. Signal Process. 62(13), 3442–3453 (2014)
Vaidya, J., Clifton, C.: Privacy-preserving k-means clustering over vertically partitioned data. In: Proceedings of the 9th International Confernce on Knowledge Discovery and Data Mining (KDD), pp. 206–215. ACM (2003)
Zaki, M.J.: Parallel and distributed data mining: an introduction. In: Zaki, M.J., Ho, C.-T. (eds.) LSPDM 1999. LNCS (LNAI), vol. 1759, pp. 1–23. Springer, Heidelberg (2000). doi:10.1007/3-540-46502-2_1
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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