2020
DOI: 10.1109/tvt.2020.2981633
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A Location Privacy-Preserving System Based on Query Range Cover-Up or Location-Based Services

Abstract: Location-based service (LBS) has been widely used in various fields of industry, and become a vital part of people's daily life. However, while providing great convenience for users, LBS results in a serious threat on users' location privacy, due to its more and more untrusted server-side. In this paper, we propose a location privacy-preserving system for LBS by constructing "cover-up ranges" to protect the query ranges associated with a location query sequence. Firstly, we present a clientbased system framewo… Show more

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Cited by 93 publications
(43 citation statements)
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“…Therefore, we believe that the use of the bHHOSRL_KELM model can make a more accurate clinical decision-making. Due to its great optimization capability, the proposed HHOSRL algorithm can also be applied to solve other problems, such as problems in video deblurring [ 73 ], microgrid planning [ 74 ], information retrieval services [ [75] , [76] , [77] ], image dehazing [ 78 ], kayak cycle phase segmentation [ 79 ], human motion capture [ 80 ], fault detection [ 81 ], video coding optimization [ 82 ], outlier detection [ 83 ], location-based services [ 84 , 85 ], image retrieval [ 86 ], multivariate time series analysis [ 87 ] and multi-objective problems [ 88 ].…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, we believe that the use of the bHHOSRL_KELM model can make a more accurate clinical decision-making. Due to its great optimization capability, the proposed HHOSRL algorithm can also be applied to solve other problems, such as problems in video deblurring [ 73 ], microgrid planning [ 74 ], information retrieval services [ [75] , [76] , [77] ], image dehazing [ 78 ], kayak cycle phase segmentation [ 79 ], human motion capture [ 80 ], fault detection [ 81 ], video coding optimization [ 82 ], outlier detection [ 83 ], location-based services [ 84 , 85 ], image retrieval [ 86 ], multivariate time series analysis [ 87 ] and multi-objective problems [ 88 ].…”
Section: Discussionmentioning
confidence: 99%
“…Due to its strong optimization capability, the developed MSMA can also be applied to other optimization problems, such as multi-objective or many optimization problems [75][76][77], big data optimization problems [78], and combination optimization problems [79]. Moreover, it can be applied to tackle the practical problems such as medical diagnosis [80][81][82][83], location-based service [84,85], service ecosystem [86], communication system conversion [87][88][89], kayak cycle phase segmentation [90], image dehazing and retrieval [91,92], information retrieval service [93][94][95], multi-view learning [96], human motion capture [97], green supplier selection [98], scheduling [99][100][101], and microgrid planning [102] problems.…”
Section: Discussionmentioning
confidence: 99%
“…In summary, our scheme significantly outperforms the existing method in almost all aspects. Therefore, our scheme may be applied in many scenarios with range query, such as cloud computing [37], [38], recommendation system [39], real-time graph stream [40], ciphertext query [41], [42], privacy-preserving for location-based services [43], continuous optimization [44], energy scheduling and optimization [45], etc.…”
Section: Comparison Of Search Efficiencymentioning
confidence: 99%