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When using differential privacy to publish high-dimensional data, the huge dimensionality leads to greater noise. Especially for high-dimensional binary data, it is easy to be covered by excessive noise. Most existing methods cannot address real high-dimensional data problems appropriately because they suffer from high time complexity. Therefore, in response to the problems above, we propose the differential privacy adaptive Bayesian network algorithm PrivABN to publish high-dimensional binarydoi:10.1155/2021/8693978 fatcat:uu2uje5rfrhe3czd6sbcxgtjba