Abstract
Data mining is a process where we can extract relevant information or patterns from the collection of data. In this era of big data, every organization aims to handle huge amounts of data and perform data mining techniques in order to extract pieces of information or patterns for various work and decision making. To protect privileged data and leakage of private information, the clients use different privacy-preserving techniques such as perturbation that protects client's data from revealing private information. The job of perturbing data on the client side is a herculean task, and it gets more difficult with the increase in the size of data. In this paper, we proposed a machine learning regression model that has been trained in such a way that it predicts the perturb data from original data and it even contains a comparative study of different regression models and their accuracy in perturbing the data.
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Dansana, J., Singh, A. (2021). A Machine Learning Approach in Data Perturbation for Privacy-Preserving Data Mining. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Computing Techniques and Applications. Smart Innovation, Systems and Technologies, vol 224. Springer, Singapore. https://doi.org/10.1007/978-981-16-1502-3_64
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DOI: https://doi.org/10.1007/978-981-16-1502-3_64
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