{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:29:42Z","timestamp":1772836182127,"version":"3.50.1"},"reference-count":33,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802161"],"award-info":[{"award-number":["61802161"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013099","name":"scientific research fund of liaoning provincial education department","doi-asserted-by":"publisher","award":["JZL202015402"],"award-info":[{"award-number":["JZL202015402"]}],"id":[{"id":"10.13039\/501100013099","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p> Location privacy refers to the individual private and sensitive location information involved in the user\u2019s access to location services. Achieving location privacy protection has become a hot topic of research. However, existing location privacy protection schemes are susceptible to background knowledge attack, edge information attack, and homogeneity attack, on the one hand, and strict constraint on the number of neighbors, on the other hand. To address these deficiencies, a dummy location interference privacy protection algorithm for Location-Based Service location is proposed. To begin with, the dummy location candidate set is constructed based on using WordNet structure to guarantee semantic differentiation, randomly selecting offset location, and conforming to probability similarity; next, the dummy location set is filtered out by discretizing dummy locations based on the Heron formula; finally, the secure anonymity set is constructed according to the anonymity level. Experiments show that the algorithm enhances the privacy protection strength and improves the security of location privacy. 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