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However, a location histogram that leaves a user\u2019s computer or device may threaten privacy when it contains visits to locations that the user does not want to disclose (<jats:italic>sensitive locations<\/jats:italic>), or when it can be used to profile the user in a way that leads to price discrimination and unsolicited advertising (e.g., as \u201cwealthy\u201d or \u201cminority member\u201d). Our work introduces two privacy notions to protect a location histogram from these threats: <jats:italic>Sensitive Location Hiding<\/jats:italic>, which aims at concealing all visits to sensitive locations, and <jats:italic>Target Avoidance\/Resemblance<\/jats:italic>, which aims at concealing the similarity\/dissimilarity of the user\u2019s histogram to a <jats:italic>target histogram<\/jats:italic> that corresponds to an undesired\/desired profile. We formulate an optimization problem around each notion: Sensitive Location Hiding (<jats:inline-formula><jats:alternatives><jats:tex-math>$${ SLH}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>SLH<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>), which seeks to construct a histogram that is as similar as possible to the user\u2019s histogram but associates all visits with nonsensitive locations, and Target Avoidance\/Resemblance (<jats:inline-formula><jats:alternatives><jats:tex-math>$${ TA}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>TA<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>\/<jats:inline-formula><jats:alternatives><jats:tex-math>$${ TR}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>TR<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>), which seeks to construct a histogram that is as dissimilar\/similar as possible to a given target histogram but remains useful for getting a good response from the application that analyzes the histogram. We develop an optimal algorithm for each notion, which operates on a notion-specific search space graph and finds a shortest or longest path in the graph that corresponds to a solution histogram. In addition, we develop a greedy heuristic for the <jats:inline-formula><jats:alternatives><jats:tex-math>$${ TA}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>TA<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>\/<jats:inline-formula><jats:alternatives><jats:tex-math>$${ TR}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>TR<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula> problem, which operates directly on a user\u2019s histogram. Our experiments demonstrate that all algorithms are effective at preserving the distribution of locations in a histogram and the quality of location recommendation. They also demonstrate that the heuristic produces near-optimal solutions while being orders of magnitude faster than the optimal algorithm for <jats:inline-formula><jats:alternatives><jats:tex-math>$${ TA}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>TA<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>\/<jats:inline-formula><jats:alternatives><jats:tex-math>$${ TR}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>TR<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>.<\/jats:p>","DOI":"10.1007\/s10115-019-01432-4","type":"journal-article","created":{"date-parts":[[2019,12,31]],"date-time":"2019-12-31T15:02:45Z","timestamp":1577804565000},"page":"2613-2651","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Location histogram privacy by Sensitive Location Hiding and Target Histogram Avoidance\/Resemblance"],"prefix":"10.1007","volume":"62","author":[{"given":"Grigorios","family":"Loukides","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2701-7809","authenticated-orcid":false,"given":"George","family":"Theodorakopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,31]]},"reference":[{"issue":"3","key":"1432_CR1","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s10115-017-1146-x","volume":"56","author":"O Abul","year":"2018","unstructured":"Abul O, Bayrak C (2018) From location to location pattern privacy in location-based services. 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