{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T12:33:14Z","timestamp":1768998794473,"version":"3.49.0"},"reference-count":28,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:p>Location publication in check-in services of geo-social networks raises serious privacy concerns due to rich sources of background information. This article proposes a novel destination prediction approach Destination Prediction specially for the check-in service of geo-social networks, which not only addresses the \u201cdata sparsity problem\u201d faced by common destination prediction approaches, but also takes advantages of the commonly available background information from geo-social networks and other public resources, such as social structure, road network, and speed limits. Further considering the Destination Prediction\u2013based attack model, we present a location privacy protection method Check-in Deletion and framework Destination Prediction\u2009+\u2009Check-in Deletion to help check-in users detect potential location privacy leakage and retain confidential locational information against destination inference attacks without sacrificing the real-time check-in precision and user experience. A new data preprocessing method is designed to construct a reasonable complete check-in subset from the worldwide check-in data set of a real-world geo-social network without loss of generality and validity of the evaluation. Experimental results show the great prediction ability of Destination Prediction approach, the effective protection capability of Check-in Deletion method against destination inference attacks, and high running efficiency of the Destination Prediction\u2009+\u2009Check-in Deletion framework.<\/jats:p>","DOI":"10.1177\/1550147716685421","type":"journal-article","created":{"date-parts":[[2017,1,21]],"date-time":"2017-01-21T17:25:15Z","timestamp":1485019515000},"page":"155014771668542","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["A novel destination prediction attack and corresponding location privacy protection method in geo-social networks"],"prefix":"10.1177","volume":"13","author":[{"given":"Di","family":"Xue","sequence":"first","affiliation":[{"name":"Institute of Command Information System, PLA University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Fa","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Command Information System, PLA University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua-Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Command Information System, PLA University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Hong","sequence":"additional","affiliation":[{"name":"Institute of Command Information System, PLA University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen-Ji","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Command Information System, PLA University of Science and Technology, Nanjing, 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