Abstract
Privacy-preserving data publishing is an important problem that has been the focus of extensive study. The state-of-the-art privacy model for this problem is differential privacy, which offers a strong degree of privacy protection without making restrictive assumptions about the adversary. In this paper, we review two methods, Privelet and PrivBayes, for improving utility in differentially private data publishing. Privelet utilizes wavelet transforms to ensure that any range-count query can be answered with noise variance that is polylogarithmic to the size of the input data domain. Meanwhile, PrivBayes employs Bayesian networks to publish high-dimensional datasets without incurring prohibitive computation overheads or excessive noise injection.
Material based on [17] and [18] appearing in TKDE and SIGMOD’14, respectively.
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Xiao, X. (2014). Differentially Private Data Release: Improving Utility with Wavelets and Bayesian Networks. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_3
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DOI: https://doi.org/10.1007/978-3-319-11116-2_3
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