Abstract
The evolution of new technologies and the spread of the Internet have led to the exchange and elaboration of massive amounts of data. Simultaneously, intelligent systems that parse and analyze patterns within data are gaining popularity. Many of these data contain sensitive information, a fact that leads to serious concerns on how such data should be managed and used from data mining techniques. Extracting knowledge from statistical databases is an essential step towards deploying intelligent systems that assist in making decisions, but also must preserve the privacy of parties involved. In this paper, we present a novel privacy preserving data mining algorithm from statistical databases that are horizontally partitioned. The novelty lies to the multi-candidate election schema and its capabilities of being a basic foundation for a privacy preserving Tree Augmented Naïve Bayesian (TAN) classifier, in order to obviate disclosure of personal information.
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Skarkala, M.E., Maragoudakis, M., Gritzalis, S., Mitrou, L. (2011). Privacy Preserving Tree Augmented Naïve Bayesian Multi-party Implementation on Horizontally Partitioned Databases. In: Furnell, S., Lambrinoudakis, C., Pernul, G. (eds) Trust, Privacy and Security in Digital Business. TrustBus 2011. Lecture Notes in Computer Science, vol 6863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22890-2_6
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DOI: https://doi.org/10.1007/978-3-642-22890-2_6
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