Abstract
In the wake of the development in science and technology, consumer or user has produced a large quantity of the information source, whether it is the mobile terminals or the client terminals. When face with such enormous data volume, some people discover the value of the data information for data mining. There are others who initiate be afraid to their privacy information learned or obtained by adversary during data propagation. On top of this, more and more people have undergone threaten of sensitive information loss exactly. Some of them, not only damnify the reputation, but also lose the benefit. Now the challenge is how to balance the security of data and the validity of it. Recently, a large amount of classification algorithms have been applied to process the data to protect data privacy and practicability, such as decision tree, Bayesian networks, support vector machine. In this paper, we overview the learning classification algorithms for privacy preserving in data mining, then make some description with the methods, function, performance evaluation.
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Acknowledgement
This work is supported by the NSFC (61300238, 61300237, 61232016, 1405254, 61373133), Marie Curie Fellowship (701697-CAR-MSCA-IFEF-ST), Basic Research Programs (Natural Science Foundation) of Jiangsu Province (BK20131004), Scientific Support Program of Jiangsu Province (BE2012473) and the PAPD fund.
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Ji, S., Wang, Z., Liu, Q., Liu, X. (2017). Classification Algorithms for Privacy Preserving in Data Mining: A Survey. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_50
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DOI: https://doi.org/10.1007/978-981-10-3023-9_50
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