Abstract
In this paper, we propose differentially private protocols for Naive Bayes classification over distributed data. Compared with existing works, the privacy and security models in the proposed protocols are stronger: firstly, both the miner and parties can be arbitrarily malicious and can collude with each other to violate the remaining honest parties privacy; secondly, all communication channels between them can be assumed to be insecure. Specifically, we build a guarantee of differential privacy into the cryptographic construction so that the proposed protocols can tolerate collusions and resist eavesdropping attacks which are caused by insecure communication channels. Additionally, the proposed protocols can be implemented at lower computation and communication costs, and some extensions to our protocols (e.g. supporting parties dynamic joins or leaves) are also proposed in this paper. Both theoretical analysis and simulation results show that the proposed privacy-preserving protocols for Naive Bayes have strong security and better classification performance than the standard one.
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Huai, M., Huang, L., Yang, W., Li, L., Qi, M. (2015). Privacy-Preserving Naive Bayes Classification. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_57
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DOI: https://doi.org/10.1007/978-3-319-25159-2_57
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