{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T20:20:33Z","timestamp":1760300433953,"version":"3.41.0"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            With the continuous exploration of genetic research, gradually exposed privacy issues become the bottleneck that limits its development. DNA motif finding is an important study to understand the regulation of gene expression; however, the existing methods generally ignore the potential sensitive information that may be exposed in the process. In this work, we utilize the\n            <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>\n                  \n                <\/jats:tex-math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\n            -differential privacy model to provide provable privacy guarantees which is independent of attackers\u2019 background knowledge. Our method makes use of sample databases to prune the generated candidate motifs to lower the magnitude of added noise. Furthermore, to improve the utility of mining results, a strategy of threshold modification is designed to reduce the propagation and random sampling errors in the mining process. Extensive experiments on actual DNA databases confirm that our approach can privately find DNA motifs with high utility and efficiency.\n          <\/jats:p>","DOI":"10.1145\/3382078","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:39:34Z","timestamp":1594125574000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Secure DNA Motif-Finding Method Based on Sampling Candidate Pruning"],"prefix":"10.1145","volume":"21","author":[{"given":"Kaijian","family":"Xia","sequence":"first","affiliation":[{"name":"School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu 221116, China; The affiliated Changshu Hospital of Soochow University (Changshu No. 1 People's Hospital), Changshu 215500, China; and School of Information and Control Engineering, China University of Mining and Technology, Jiangsu, Xuzhou 221116, China"}]},{"given":"Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu 221116, China"}]},{"given":"Yaqing","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu 221116, China"}]},{"given":"Huanhuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Medical Informatics, Xuzhou Medical University, Xuzhou, Jiangsu 221116, China; and School of Information and Control Engineering, China University of Mining and Technology, Jiangsu, Xuzhou 221116, China"}]}],"member":"320","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-8-S7-S21"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2007.75"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2013.0054"},{"key":"e_1_2_1_4_1","article-title":"A genetic optimization approach for finding common motif in biological sequences","volume":"2","author":"Vishal V.","year":"2011","unstructured":"V. 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In 2016 IEEE 32nd International Conference on Data Engineering (ICDE\u201916), IEEE, 229\u2013240."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2601106"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2013.12.016"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559850"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/645484.656379"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/645920.672836"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.14778\/2428536.2428539"},{"key":"e_1_2_1_30_1","unstructured":"M. Wistuba J. Grabocka and L. Schmidt-Thieme Ultra-fast shapelets for time series classification. arXiv:1503.05018. Retrieved from http:\/\/de.arxiv.org\/pdf\/1503.05018.  M. Wistuba J. Grabocka and L. 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