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When selecting real users from the surrounding area to co-construct anonymous regions, it is easy to cause the leakage of user location information. Moreover, using false addresses to construct a region requires calculating the probability of location queries, which increases the computational complexity. In this paper, an all-dummy k-anonymous algorithm based on location offset is proposed to construct anonymous regions. This algorithm randomly selects k\u22121 locations and real users in the selected anonymous compose an anonymous group at first. Subsequently, these coordinates are centered on migration, generating multiple dummy addresses of each location migration, such that the dummy address distance is greater than the radius of the user's query, with the dummy address location information used for the location server queries. Through experimental verification, compared with the circle-based dummy address generation algorithm and the random k-anonymous algorithm, the all-dummy k-anonymous algorithm is found to achieve an entropy value and tracking success rate closer to the optimal k-anonymous algorithm without increasing the communication cost.<\/jats:p>","DOI":"10.1007\/s00607-022-01067-4","type":"journal-article","created":{"date-parts":[[2022,3,13]],"date-time":"2022-03-13T07:02:37Z","timestamp":1647154957000},"page":"1739-1751","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["All-dummy k-anonymous privacy protection algorithm based on location offset"],"prefix":"10.1007","volume":"104","author":[{"given":"Jianghui","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2897-8372","authenticated-orcid":false,"given":"Shengxiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,13]]},"reference":[{"issue":"11","key":"1067_CR1","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMC.2018.2811481","volume":"17","author":"J Chen","year":"2018","unstructured":"Chen J, He K, Yuan Q (2018) Blind filtering at third parties: an efficient privacy-preserving framework for location-based services. 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