Abstract
In data analysis, providing privacy to the customer data is an important issue. In this paper, a large number of customers are surveyed to learn the rules of classification on their data while preserving privacy of the customers. Randomization techniques were proposed to address this problem. These techniques provide more accuracy for less privacy of customers, conversely less accuracy for more privacy of the customers. In this paper, we propose a cryptographic approach with strong privacy and no loss of accuracy as a cost of privacy for continuous data. Our approach uses naive Bayes classification algorithm using frequency mining. The result shows the efficiency of our approach.
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Durga Prasad, K., Adi Narayana Reddy, K., Vasumathi, D. (2019). Privacy-Preserving Naive Bayesian Classifier for Continuous Data and Discrete Data. In: Bapi, R., Rao, K., Prasad, M. (eds) First International Conference on Artificial Intelligence and Cognitive Computing . Advances in Intelligent Systems and Computing, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-13-1580-0_28
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DOI: https://doi.org/10.1007/978-981-13-1580-0_28
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