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In order to prevent location information from being leaked after monitoring and collection, location privacy must be effectively protected. Therefore, this paper proposes a privacy protection method based on location sensitivity for location recommendation. This method uses location trajectories and check-in frequencies to set a threshold so as to classify location sensitivity levels. The corresponding privacy budget is then assigned based on the sensitivity to add Laplace noise that satisfies the differential privacy. Experimental results show that this method can effectively protect the user\u2019s location privacy and reduce the impact of differential privacy noise on service quality.<\/jats:p>","DOI":"10.1186\/s13638-019-1606-y","type":"journal-article","created":{"date-parts":[[2019,12,10]],"date-time":"2019-12-10T11:04:07Z","timestamp":1575975847000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Location recommendation privacy protection method based on location sensitivity division"],"prefix":"10.1186","volume":"2019","author":[{"given":"Chunyong","family":"Yin","sequence":"first","affiliation":[]},{"given":"Xiaokang","family":"Ju","sequence":"additional","affiliation":[]},{"given":"Zhichao","family":"Yin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5473-8738","authenticated-orcid":false,"given":"Jin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,10]]},"reference":[{"issue":"1","key":"1606_CR1","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.future.2017.09.015","volume":"78","author":"M Eirinaki","year":"2018","unstructured":"M. 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