Location privacy protection method based on lightweight K-anonymity incremental nearest neighbor algorithm
The use of location-based service brings convenience to people’s daily lives, but it also raises concerns about users’ location privacy.In the k-nearest neighbor query problem, constructing K-anonymizing spatial regions is a method used to protects users’ location privacy, but it results in a large...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2023-06-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023038 |
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| _version_ | 1841529586955845632 |
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| author | Saite CHEN Weihai LI Yuanzhi YAO Nenghai YU |
| author_facet | Saite CHEN Weihai LI Yuanzhi YAO Nenghai YU |
| author_sort | Saite CHEN |
| collection | DOAJ |
| description | The use of location-based service brings convenience to people’s daily lives, but it also raises concerns about users’ location privacy.In the k-nearest neighbor query problem, constructing K-anonymizing spatial regions is a method used to protects users’ location privacy, but it results in a large waste of communication overhead.The SpaceTwist scheme is an alternative method that uses an anchor point instead of the real location to complete the k-nearest neighbor query,which is simple to implement and has less waste of communication overhead.However,it cannot guarantee K-anonymous security, and the specific selection method of the anchor point is not provided.To address these shortcomings in SpaceTwist, some schemes calculate the user’s K-anonymity group by introducing a trusted anonymous server or using the way of user collaboration, and then enhance the end condition of the query algorithm to achieve K-anonymity security.Other schemes propose the anchor point optimization method based on the approximate distribution of interest points, which can further reduce the average communication overhead.A lightweight K-anonymity incremental nearest neighbor (LKINN) location privacy protection algorithm was proposed to improve SpaceTwist.LKINN used convex hull mathematical tool to calculate the key points of K-anonymity group, and proposed an anchor selection method based on it, achieving K-anonymity security with low computational and communication costs.LKINN was based on a hybrid location privacy protection architecture, making only semi-trusted security assumptions for all members of the system, which had lax security assumptions compared to some existing research schemes.Simulation results show that LKINN can prevent semi-trusted users from stealing the location privacy of normal users and has smaller query response time and communication overhead compare to some existing schemes. |
| format | Article |
| id | doaj-art-0fe7d899e68645a3a07b4c7546dcb803 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2023-06-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-0fe7d899e68645a3a07b4c7546dcb8032025-01-15T03:16:36ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2023-06-019607259578164Location privacy protection method based on lightweight K-anonymity incremental nearest neighbor algorithmSaite CHENWeihai LIYuanzhi YAONenghai YUThe use of location-based service brings convenience to people’s daily lives, but it also raises concerns about users’ location privacy.In the k-nearest neighbor query problem, constructing K-anonymizing spatial regions is a method used to protects users’ location privacy, but it results in a large waste of communication overhead.The SpaceTwist scheme is an alternative method that uses an anchor point instead of the real location to complete the k-nearest neighbor query,which is simple to implement and has less waste of communication overhead.However,it cannot guarantee K-anonymous security, and the specific selection method of the anchor point is not provided.To address these shortcomings in SpaceTwist, some schemes calculate the user’s K-anonymity group by introducing a trusted anonymous server or using the way of user collaboration, and then enhance the end condition of the query algorithm to achieve K-anonymity security.Other schemes propose the anchor point optimization method based on the approximate distribution of interest points, which can further reduce the average communication overhead.A lightweight K-anonymity incremental nearest neighbor (LKINN) location privacy protection algorithm was proposed to improve SpaceTwist.LKINN used convex hull mathematical tool to calculate the key points of K-anonymity group, and proposed an anchor selection method based on it, achieving K-anonymity security with low computational and communication costs.LKINN was based on a hybrid location privacy protection architecture, making only semi-trusted security assumptions for all members of the system, which had lax security assumptions compared to some existing research schemes.Simulation results show that LKINN can prevent semi-trusted users from stealing the location privacy of normal users and has smaller query response time and communication overhead compare to some existing schemes.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023038location-based servicelocation privacy preservationK-anonymityconvex hullanchor |
| spellingShingle | Saite CHEN Weihai LI Yuanzhi YAO Nenghai YU Location privacy protection method based on lightweight K-anonymity incremental nearest neighbor algorithm 网络与信息安全学报 location-based service location privacy preservation K-anonymity convex hull anchor |
| title | Location privacy protection method based on lightweight K-anonymity incremental nearest neighbor algorithm |
| title_full | Location privacy protection method based on lightweight K-anonymity incremental nearest neighbor algorithm |
| title_fullStr | Location privacy protection method based on lightweight K-anonymity incremental nearest neighbor algorithm |
| title_full_unstemmed | Location privacy protection method based on lightweight K-anonymity incremental nearest neighbor algorithm |
| title_short | Location privacy protection method based on lightweight K-anonymity incremental nearest neighbor algorithm |
| title_sort | location privacy protection method based on lightweight k anonymity incremental nearest neighbor algorithm |
| topic | location-based service location privacy preservation K-anonymity convex hull anchor |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023038 |
| work_keys_str_mv | AT saitechen locationprivacyprotectionmethodbasedonlightweightkanonymityincrementalnearestneighboralgorithm AT weihaili locationprivacyprotectionmethodbasedonlightweightkanonymityincrementalnearestneighboralgorithm AT yuanzhiyao locationprivacyprotectionmethodbasedonlightweightkanonymityincrementalnearestneighboralgorithm AT nenghaiyu locationprivacyprotectionmethodbasedonlightweightkanonymityincrementalnearestneighboralgorithm |