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Database Syst."],"published-print":{"date-parts":[[2017,12,31]]},"abstract":"<jats:p>Privacy-preserving data publishing is an important problem that has been the focus of extensive study. The state-of-the-art solution for this problem is differential privacy, which offers a strong degree of privacy protection without making restrictive assumptions about the adversary. 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 require injecting a prohibitive amount of noise compared to the signal in the data, which renders the published data next to useless.<\/jats:p>\n                  <jats:p>\n                    To address the deficiency of the existing methods, this paper presents P\n                    <jats:sc>riv<\/jats:sc>\n                    B\n                    <jats:sc>ayes<\/jats:sc>\n                    , a differentially private method for releasing high-dimensional data. Given a dataset\n                    <jats:italic>D<\/jats:italic>\n                    , P\n                    <jats:sc>riv<\/jats:sc>\n                    B\n                    <jats:sc>ayes<\/jats:sc>\n                    first constructs a Bayesian network\n                    <jats:italic>N<\/jats:italic>\n                    , which (i) provides a succinct model of the correlations among the attributes in\n                    <jats:italic>D<\/jats:italic>\n                    and (ii) allows us to approximate the distribution of data in\n                    <jats:italic>D<\/jats:italic>\n                    using a set\n                    <jats:italic>P<\/jats:italic>\n                    of low-dimensional marginals of\n                    <jats:italic>D<\/jats:italic>\n                    . After that, P\n                    <jats:sc>riv<\/jats:sc>\n                    B\n                    <jats:sc>ayes<\/jats:sc>\n                    injects noise into each marginal in\n                    <jats:italic>P<\/jats:italic>\n                    to ensure differential privacy and then uses the noisy marginals and the Bayesian network to construct an approximation of the data distribution in\n                    <jats:italic>D<\/jats:italic>\n                    . Finally, P\n                    <jats:sc>riv<\/jats:sc>\n                    B\n                    <jats:sc>ayes<\/jats:sc>\n                    samples tuples from the approximate distribution to construct a synthetic dataset, and then releases the synthetic data. Intuitively, P\n                    <jats:sc>riv<\/jats:sc>\n                    B\n                    <jats:sc>ayes<\/jats:sc>\n                    circumvents the curse of dimensionality, as it injects noise into the low-dimensional marginals in\n                    <jats:italic>P<\/jats:italic>\n                    instead of the high-dimensional dataset\n                    <jats:italic>D<\/jats:italic>\n                    . Private construction of Bayesian networks turns out to be significantly challenging, and we introduce a novel approach that uses a surrogate function for mutual information to build the model more accurately. We experimentally evaluate P\n                    <jats:sc>riv<\/jats:sc>\n                    B\n                    <jats:sc>ayes<\/jats:sc>\n                    on real data and demonstrate that it significantly outperforms existing solutions in terms of accuracy.\n                  <\/jats:p>","DOI":"10.1145\/3134428","type":"journal-article","created":{"date-parts":[[2017,10,27]],"date-time":"2017-10-27T08:48:13Z","timestamp":1509094093000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":371,"title":["PrivBayes"],"prefix":"10.1145","volume":"42","author":[{"given":"Jun","family":"Zhang","sequence":"first","affiliation":[{"name":"Google Inc., China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Graham","family":"Cormode","sequence":"additional","affiliation":[{"name":"University of Warwick, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cecilia M.","family":"Procopiuc","sequence":"additional","affiliation":[{"name":"Google Inc., USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Divesh","family":"Srivastava","sequence":"additional","affiliation":[{"name":"AT8T Labs--Research, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaokui","family":"Xiao","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,10,27]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"UCI Machine Learning Repository","author":"Bache Kevin","year":"2013","unstructured":"Kevin Bache and Moshe Lichman . 2013. 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