{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:43:40Z","timestamp":1772113420416,"version":"3.50.1"},"reference-count":20,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:00:00Z","timestamp":1626480000000},"content-version":"vor","delay-in-days":197,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>When using differential privacy to publish high\u2010dimensional data, the huge dimensionality leads to greater noise. Especially for high\u2010dimensional binary data, it is easy to be covered by excessive noise. Most existing methods cannot address real high\u2010dimensional 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\u2010dimensional binary data. This algorithm uses a new greedy algorithm to accelerate the construction of Bayesian networks, which reduces the time complexity of the GreedyBayes algorithm from  to <jats:italic>O<\/jats:italic>(<jats:italic>n<\/jats:italic><jats:italic>m<\/jats:italic><jats:sup>4<\/jats:sup>). In addition, it uses an adaptive algorithm to adjust the structure and uses a differential privacy Exponential mechanism to preserve the privacy, so as to generate a high\u2010quality protected Bayesian network. Moreover, we use the Bayesian network to calculate the conditional distribution with noise and generate a synthetic dataset for publication. This synthetic dataset satisfies <jats:italic>\u03b5<\/jats:italic>\u2010differential privacy. Lastly, we carry out experiments against three real\u2010life high\u2010dimensional binary datasets to evaluate the functional performance.<\/jats:p>","DOI":"10.1155\/2021\/8693978","type":"journal-article","created":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T18:20:06Z","timestamp":1626546006000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Equation Chapter 1 Section 1 Differentially Private High\u2010Dimensional Binary Data Publication via Adaptive Bayesian Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9583-6040","authenticated-orcid":false,"given":"Sun","family":"Lan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6596-8926","authenticated-orcid":false,"given":"Jinxin","family":"Hong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3431-1857","authenticated-orcid":false,"given":"Junya","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1575-8239","authenticated-orcid":false,"given":"Jianping","family":"Cai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1826-6707","authenticated-orcid":false,"given":"Yilei","family":"Wang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,7,17]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488502001648"},{"key":"e_1_2_10_2_2","doi-asserted-by":"crossref","unstructured":"WongR. 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