DPLQ: Location‐based service privacy protection scheme based on differential privacy
Abstract The existing privacy protection schemes for Location‐Based Service (LBS) only protect users' location privacy or query privacy, which can not adopt both of the privacy protections simultaneously in the LBS system. Moreover, these schemes cannot take into account the spatial‐temporal co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2021-11-01
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| Series: | IET Information Security |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ise2.12034 |
| _version_ | 1797422231112908800 |
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| author | Qingyun Zhang Xing Zhang Mingyue Wang Xiaohui Li |
| author_facet | Qingyun Zhang Xing Zhang Mingyue Wang Xiaohui Li |
| author_sort | Qingyun Zhang |
| collection | DOAJ |
| description | Abstract The existing privacy protection schemes for Location‐Based Service (LBS) only protect users' location privacy or query privacy, which can not adopt both of the privacy protections simultaneously in the LBS system. Moreover, these schemes cannot take into account the spatial‐temporal correlation and background knowledge. In response to the above mentioned questions, the LBS Privacy Protection Scheme Based on Differential Privacy (DPLQ) is proposed. The method contains two kinds of privacy protection algorithms: users' location privacy protection algorithm and users' query privacy protection algorithm. The users' location privacy protection algorithm divides the map using the Voronoi diagram, choosing l fake location points based on the improved k‐means algorithm and l‐diversity idea, and protects users' location privacy with the Laplace mechanism. Based on the k‐anonymous algorithm, the users' query privacy protection algorithm builds a query k‐anonymous set according to the neighbour users' query requests at the same time t in the cluster and the historical query probability of the region’s POI and protects users' query privacy with the exponential mechanism. Through setting the privacy protection intensity of the algorithm by the users, the generated location dataset and query k‐anonymous set can resist a variety of attacks from malicious attackers. Theoretical analysis and experimental results show that the scheme can effectively protect the location privacy and query privacy of users. |
| first_indexed | 2024-03-09T07:29:14Z |
| format | Article |
| id | doaj.art-6d053f7e6374470188a09caf2ab44201 |
| institution | Directory Open Access Journal |
| issn | 1751-8709 1751-8717 |
| language | English |
| last_indexed | 2024-03-09T07:29:14Z |
| publishDate | 2021-11-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Information Security |
| spelling | doaj.art-6d053f7e6374470188a09caf2ab442012023-12-03T06:34:18ZengHindawi-IETIET Information Security1751-87091751-87172021-11-0115644245610.1049/ise2.12034DPLQ: Location‐based service privacy protection scheme based on differential privacyQingyun Zhang0Xing Zhang1Mingyue Wang2Xiaohui Li3College of Electronics and Information Engineering Liaoning University of Technology Jinzhou ChinaCollege of Electronics and Information Engineering Liaoning University of Technology Jinzhou ChinaCollege of Electronics and Information Engineering Liaoning University of Technology Jinzhou ChinaCollege of Electronics and Information Engineering Liaoning University of Technology Jinzhou ChinaAbstract The existing privacy protection schemes for Location‐Based Service (LBS) only protect users' location privacy or query privacy, which can not adopt both of the privacy protections simultaneously in the LBS system. Moreover, these schemes cannot take into account the spatial‐temporal correlation and background knowledge. In response to the above mentioned questions, the LBS Privacy Protection Scheme Based on Differential Privacy (DPLQ) is proposed. The method contains two kinds of privacy protection algorithms: users' location privacy protection algorithm and users' query privacy protection algorithm. The users' location privacy protection algorithm divides the map using the Voronoi diagram, choosing l fake location points based on the improved k‐means algorithm and l‐diversity idea, and protects users' location privacy with the Laplace mechanism. Based on the k‐anonymous algorithm, the users' query privacy protection algorithm builds a query k‐anonymous set according to the neighbour users' query requests at the same time t in the cluster and the historical query probability of the region’s POI and protects users' query privacy with the exponential mechanism. Through setting the privacy protection intensity of the algorithm by the users, the generated location dataset and query k‐anonymous set can resist a variety of attacks from malicious attackers. Theoretical analysis and experimental results show that the scheme can effectively protect the location privacy and query privacy of users.https://doi.org/10.1049/ise2.12034query processingcomputational geometrydata protectionlocation based services |
| spellingShingle | Qingyun Zhang Xing Zhang Mingyue Wang Xiaohui Li DPLQ: Location‐based service privacy protection scheme based on differential privacy IET Information Security query processing computational geometry data protection location based services |
| title | DPLQ: Location‐based service privacy protection scheme based on differential privacy |
| title_full | DPLQ: Location‐based service privacy protection scheme based on differential privacy |
| title_fullStr | DPLQ: Location‐based service privacy protection scheme based on differential privacy |
| title_full_unstemmed | DPLQ: Location‐based service privacy protection scheme based on differential privacy |
| title_short | DPLQ: Location‐based service privacy protection scheme based on differential privacy |
| title_sort | dplq location based service privacy protection scheme based on differential privacy |
| topic | query processing computational geometry data protection location based services |
| url | https://doi.org/10.1049/ise2.12034 |
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