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Differential Privacy is the state-of-the-art goal for the problem of privacy-preserving data release and privacy-preserving data mining.Existing techniques using differential privacy,however,cannot effectively handle the publication of high-dimensional data.In particular,when the input dataset contains a large number of attributes,existing methods incur higher computing complexity and lower information to noise ratio,which renders the published data next to useless.This proposa aims to reduce computing complexity and signal to noise ratio.The starting point is to approximate the full distribution of high-dimensional dataset with a set of low-dimensional marginaldistributions via optimizing score function and reducing sensitivity,in which generation of noisy conditional distributions with differential privacy is computed in a set of low-dimensional subspaces,and then,the sample tuples from the noisy approximation distribution are used to generate and release the synthetic dataset.Some crucial science problems would be investigated below:(i)constructing a low k-degree Bayesian network over the high-dimensional dataset via exponential mechanism in differential privacy,where the score function is optimized to reduce the sensitivity using mutual information,equivalence classes in maximum joint distribution and dynamic programming;(ii)studying the algorithm to compute a set of noisy conditional distributions from joint distributions in the subspace of Bayesian network,via the Laplace mechanism of differential privacy.(iii)exploring how to generate synthetic data from thedifferentially private Bayesian network and conditional distributions,without explicitly materializing the noisy global distribution.The proposed solution may have theoretical and technical significance for synthetic data generation with differential privacy on business prospects.
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