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However, when users benefit from LBSs, the collection and analysis of users\u2019 location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his\/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi>k<\/mml:mi><\/mml:math>-anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.<\/jats:p>","DOI":"10.1155\/2020\/8892079","type":"journal-article","created":{"date-parts":[[2020,7,18]],"date-time":"2020-07-18T23:31:56Z","timestamp":1595115116000},"page":"1-16","source":"Crossref","is-referenced-by-count":12,"title":["Location Privacy-Preserving Method Based on Historical Proximity Location"],"prefix":"10.1155","volume":"2020","author":[{"given":"Xueying","family":"Guo","sequence":"first","affiliation":[{"name":"College of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"given":"Wenming","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Nanjing University of Posts and 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