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Josenildo Costa da Silva, Matthias Klusch, Stefano Lodi, Gianluca Moro
Agent technology could provide a computing paradigm which naturally fits distributed data mining environments. In particular, spontaneous formation of peer-to-peer agent-based data mining systems seems a plausible scenario in years to come. However, the emergence of peer-to-peer environments further aggravates privacy and security concerns that arise when performing data mining tasks. We analyze potential threats to data privacy in a peer-to-peer agent-based distributed data mining scenario under the hypothesis that all the distributed datasets follow the same schema, and discuss inference attacks which could compromise data privacy in a peer-to-peer distributed clustering scheme known as KDEC.
Keywords: inference attack, peer-to-peer, distributed data mining, privacy, data clustering
Citation: Josenildo Costa da Silva, Matthias Klusch, Stefano Lodi, Gianluca Moro: Inference Attacks in Peer-to-Peer Homogeneous Distributed Data Mining. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.450-454.
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