{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:22:42Z","timestamp":1750220562089,"version":"3.41.0"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"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":[[2022,8,31]]},"abstract":"<jats:p>Nowadays, with the rapid development of intelligent technology, it is urgent to effectively prevent infectious diseases and ensure people's privacy. The present work constructs the intelligent prevention system of infectious diseases based on edge computing by using the edge computing algorithm, and further deploys and optimizes the privacy information security defense strategy of users in the system, controls the cost, constructs the optimal conditions of the system security defense, and finally analyzes the performance of the model. The results show that the system delay decreases with the increase of power in the downlink. In the analysis of the security performance of personal privacy information, it is found that six different nodes can maintain the optimal strategy when the cost is minimized in the finite time domain and infinite time domain. In comparison with other classical algorithms in the communication field, when the intelligent prevention system of infectious diseases constructed adopts the best defense strategy, it can effectively reduce the consumption of computing resources of edge network equipment, and the prediction accuracy is obviously better than that of other algorithms, reaching 83%. Hence, the results demonstrate that the model constructed can ensure the safety performance and forecast accuracy, and achieve the best defense strategy at low cost, which provides experimental reference for the prevention and detection of infectious diseases in the later period.<\/jats:p>","DOI":"10.1145\/3475869","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T23:40:36Z","timestamp":1638229236000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Edge Computing to Solve Security Issues for Infectious Disease Intelligence Prevention"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8164-1405","authenticated-orcid":false,"given":"Zhihan","family":"Lv","sequence":"first","affiliation":[{"name":"School of Data Science and Software Engineering, Qingdao University, Qingdao, China"}]},{"given":"Ranran","family":"Lou","sequence":"additional","affiliation":[{"name":"School of Data Science and Software Engineering, Qingdao University, Qingdao, China"}]},{"given":"Haibin","family":"Lv","sequence":"additional","affiliation":[{"name":"North China Sea Offshore Engineering Survey Institute, Ministry of Natural Resources North Sea Bureau, Qingdao, China"}]}],"member":"320","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000458"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2920942"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3005638"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/SEC.2018.00018"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2018.12.009"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2851751"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2019.1800234"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.09.031"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600863"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2805263"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2913438"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3019975"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2017.06.037"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017162326"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2017.11.018"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3012139"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2957127"},{"key":"e_1_3_1_19_2","article-title":"BRWMDA: Predicting microbe-disease associations based on similarities and bi-random walk on disease and microbe networks","author":"Yan C.","year":"2019","unstructured":"C. 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