PRIVACY-PRESERVING DATA MINING OF DISTRIBUTED DATABASES USING NAÏVE BAYES CLASSIFIER | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 14, Volume 41, No 4, July and August 2013, Page 1581-1594 PDF (358.42 K) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2013.114882 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mohamed A. Ouda  ; Sameh A. Salem
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| Depart. of Communication and Computer, Faculty of Engineering Helwan University, Cairo – Egypt | ||||
| Abstract | ||||
| Privacy-preserving data mining is discovering accurate patterns and rules without precise access to the original data. In this paper, we propose a novel algorithm for privacy preserving data mining. The proposed algorithm is based on the integration of RSA public key cryptosystem and homomorphic encryption scheme. No data is shared between distributed parties except the final result. Data mining algorithm is performed locally for each party. The final result of all parties is compared to get the target value. Previous solution for privacy preserving data mining of Naive Bayes classifier is based on secure sum that may permit collusion between parties, which is not here in proposed solution. Theoretical analysis and experimental results show that the proposed algorithm can provide good capability of privacy preserving, accuracy and efficiency. | ||||
| Keywords | ||||
| privacy preserving; Naive Bayes classifier; distributed databases; secure multiparty computation | ||||
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