Abstract
With technological revolution, a huge amount of data is being collected and as a consequence the need of mining knowledge from this data is triggered. But, data in its raw form comprises of sensitive information and advances in data mining techniques have increased the privacy breach. However, due to socio-technical transformations, most countries have levied the guidelines and policies for publishing certain data. As a result, a new area known as Privacy Preserving Data Mining (PPDM) has emerged. The goal of PPDM is to extract valuable information from data while retaining privacy of this data. The paper focuses on exploring PPDM in different aspects, such as types of privacy, PPDM scenarios and applications, methods of evaluating PPDM algorithms etc. Also, the paper shows parametric analysis and comparison of different PPDM techniques. The goal of this study is to facilitate better understanding of these PPDM techniques and boost fruitful research in this direction.
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Patel, D., Kotecha, R. (2017). Privacy Preserving Data Mining: A Parametric Analysis. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-10-3156-4_14
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DOI: https://doi.org/10.1007/978-981-10-3156-4_14
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