Abstract
Negative database (NDB) is a new type of information representation method that protects private data by storing data in the raw data complement set. The KNN classification algorithm is a classic classification algorithm, and the Euclidean distance formula is one of the most commonly used distance calculation formulas in classification algorithms. However, the distance calculation method for the existing KNN classification algorithm based on negative database is the one-hot coded Hamming distance formula. For this encoding method, when data set have many attributes, the length of the binary string becomes extremely long after encoding, thereby it increases the computational cost and complexity of the classification algorithm. In this paper, we proposed a KNN classification algorithm based on the Euclidean distance formula on the negative database, which is used to complete the classification research under the premise of protecting data security. The experimental results show that the algorithm in this paper achieves high classification accuracy.
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Liao, H., Chen, Y., Bu, S., Zhang, M. (2020). Privacy-Protected KNN Classification Algorithm Based on Negative Database. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_7
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DOI: https://doi.org/10.1007/978-3-030-32591-6_7
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