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Toward Answering Federated Spatial Range Queries Under Local Differential Privacy.

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Title: Toward Answering Federated Spatial Range Queries Under Local Differential Privacy.
Authors: Feng, Guanghui1 (AUTHOR), Wang, Guojun1 (AUTHOR) csgjwang@gzhu.edu.cn, Peng, Tao1 (AUTHOR), Vocaturo, Eugenio1 (AUTHOR)
Source: International Journal of Intelligent Systems. 10/26/2024, Vol. 2024, p1-27. 27p.
Subjects: Data release, Data analytics, Privacy, Probability theory, Encoding
Abstract: Federated analytics (FA) over spatial data with local differential privacy (LDP) has attracted considerable research attention recently. Existing solutions for this problem mostly employ a uniform grid (UG) structure, which recursively decomposes the whole spatial domain into fine‐grained regions in the distributed setting. In each round, the sampled clients perturb their locations using a random response mechanism with a fixed probability. This approach, however, cannot encode the client's location effectively and will lead to ill‐suited query results. To address the deficiency of existing solutions, we propose LDP‐FSRQ, a spatial range query algorithm that relies on a hybrid spatial structure composed of the UG and quad‐tree with nonuniform perturbation (NUP) probability to encode and perturb clients' locations. In each iteration of LDP‐FSRQ, each client adopts the quad‐tree to encode his/her location into a binary string and uses four local perturbation mechanisms to protect the encoded string. Then, the collector prunes the quad‐tree of the current round according to the clients' reports and shares the pruned tree with the clients of the next round. We demonstrate the application of LDP‐FSRQ on Beijing, Landmark, Check‐in, and NYC datasets, and the experimental results show that our approach outperforms its competitors in terms of queries' utility. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Toward Answering Federated Spatial Range Queries Under Local Differential Privacy.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 10/26/2024, Vol. 2024, p1-27. 27p.
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  Data: <searchLink fieldCode="DE" term="%22Data+release%22">Data release</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Encoding%22">Encoding</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Federated analytics (FA) over spatial data with local differential privacy (LDP) has attracted considerable research attention recently. Existing solutions for this problem mostly employ a uniform grid (UG) structure, which recursively decomposes the whole spatial domain into fine‐grained regions in the distributed setting. In each round, the sampled clients perturb their locations using a random response mechanism with a fixed probability. This approach, however, cannot encode the client's location effectively and will lead to ill‐suited query results. To address the deficiency of existing solutions, we propose LDP‐FSRQ, a spatial range query algorithm that relies on a hybrid spatial structure composed of the UG and quad‐tree with nonuniform perturbation (NUP) probability to encode and perturb clients' locations. In each iteration of LDP‐FSRQ, each client adopts the quad‐tree to encode his/her location into a binary string and uses four local perturbation mechanisms to protect the encoded string. Then, the collector prunes the quad‐tree of the current round according to the clients' reports and shares the pruned tree with the clients of the next round. We demonstrate the application of LDP‐FSRQ on Beijing, Landmark, Check‐in, and NYC datasets, and the experimental results show that our approach outperforms its competitors in terms of queries' utility. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/2024/2408270
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      – Code: eng
        Text: English
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        PageCount: 27
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        Type: general
      – SubjectFull: Data analytics
        Type: general
      – SubjectFull: Privacy
        Type: general
      – SubjectFull: Probability theory
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      – SubjectFull: Encoding
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      – TitleFull: Toward Answering Federated Spatial Range Queries Under Local Differential Privacy.
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            – D: 26
              M: 10
              Text: 10/26/2024
              Type: published
              Y: 2024
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