Abstract
Many services for data analysis require customer’s data to be exposed and privacy issues are critical in related fields. To address this problem, we propose a Privacy-Preserving Naive Bayes classifier (PP-NBC) model which provides classification results without leaking privacy information in data sources. Through classification process in PP-NBC, the operations are evaluated using encrypted data by applying fully homomorphic encryption scheme so that service providers are able to handle customer’s data without knowing their actual values. The proposed method is implemented with a homomorphic encryption library called HElib and we carry out a primitive performance evaluation for the proposed PP-NBC.
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Kim, S., Omori, M., Hayashi, T., Omori, T., Wang, L., Ozawa, S. (2018). Privacy-Preserving Naive Bayes Classification Using Fully Homomorphic Encryption. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_30
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DOI: https://doi.org/10.1007/978-3-030-04212-7_30
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