Abstract
In recent past, the data are generated massively from medical sector due to the advancements and growth of technology leading to high-dimensional and massive data. Handling the medical data is crucial since it contains some sensitive data of the individuals. If the sensitive data is revealed to the adversaries or others then that may be vulnerable to attack. Hiding the huge volume of data is practically difficult task among the researchers. Therefore, in real life only the sensitive data are hidden from the huge volume of data for providing security since hiding the entire data is costlier. Therefore, the sensitive attributes must be identified from the huge volume of data in order to hide them for preserving the privacy. In order to identify the sensitive data to hide them for providing security, reducing the computational and transmission cost in secured data transmission, this chapter presents a pragmatic approach to identify the sensitive attributes for preserving privacy. This proposed method is tested on the various real-world datasets with different classifiers and also the results are presented.
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Tamizhpoonguil, B., Singh, D.A.A.G., Leavline, E.J. (2017). Identifying Sensitive Attributes for Preserving Privacy. In: Dash, S., Vijayakumar, K., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-10-3174-8_54
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DOI: https://doi.org/10.1007/978-981-10-3174-8_54
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