Abstract
Data mining includes extracting useful and interesting patterns from large dataset, to create and enhance decision support systems. Due to this, data mining has become an important component in various fields of day-to-day life including medicine, business, education, science and so on. Numerous data mining techniques have been developed. These techniques make the privacy preservation an important issue. When applying privacy preservation techniques, importance is given to the utility and information loss. In this paper we propose Preference Imposed Individual Ranking based microaggregation with Optimal Noise addition technique (P-IRON) for anonymizing the individual records. Through the experimental results, our proposed technique is validated to prevent the disclosure of sensitive data without degradation of data utilization. Our work highlights some discussions about future work and promising directions in the perspective of privacy preservation in data mining.
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Arumugam, G., Jane Varamani Sulekha, V. (2017). P-IRON for Privacy Preservation in Data Mining. In: Uden, L., Lu, W., Ting, IH. (eds) Knowledge Management in Organizations. KMO 2017. Communications in Computer and Information Science, vol 731. Springer, Cham. https://doi.org/10.1007/978-3-319-62698-7_34
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DOI: https://doi.org/10.1007/978-3-319-62698-7_34
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