Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server
In this paper, we study the privacy-preserving task assignment in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy notion for location-based systems). Different from the pre...
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Published in | arXiv.org |
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23.08.2021
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Abstract | In this paper, we study the privacy-preserving task assignment in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy notion for location-based systems). Different from the previously studied online setting, where each task is assigned immediately upon arrival, we target the batch-based setting, where the server maximizes the number of successfully assigned tasks after a batch of tasks arrive. To achieve this goal, we propose the k-Switch solution, which first divides the workers into small groups based on the perturbed distance between workers/tasks, and then utilizes Homomorphic Encryption (HE) based secure computation to enhance the task assignment. Furthermore, we expedite HE-based computation by limiting the size of the small groups under k. Extensive experiments demonstrate that, in terms of the number of successfully assigned tasks, the k-Switch solution improves batch-based baselines by 5.9X and the existing online solution by 1.74X, with no privacy leak. |
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AbstractList | In this paper, we study the privacy-preserving task assignment in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy notion for location-based systems). Different from the previously studied online setting, where each task is assigned immediately upon arrival, we target the batch-based setting, where the server maximizes the number of successfully assigned tasks after a batch of tasks arrive. To achieve this goal, we propose the k-Switch solution, which first divides the workers into small groups based on the perturbed distance between workers/tasks, and then utilizes Homomorphic Encryption (HE) based secure computation to enhance the task assignment. Furthermore, we expedite HE-based computation by limiting the size of the small groups under k. Extensive experiments demonstrate that, in terms of the number of successfully assigned tasks, the k-Switch solution improves batch-based baselines by 5.9X and the existing online solution by 1.74X, with no privacy leak. |
Author | Zheng, Libin Li, Maocheng Wu, Han Chen, Lei Wang, Jiachuan Cheng, Peng Lin, Xuemin |
Author_xml | – sequence: 1 givenname: Maocheng surname: Li fullname: Li, Maocheng – sequence: 2 givenname: Jiachuan surname: Wang fullname: Wang, Jiachuan – sequence: 3 givenname: Libin surname: Zheng fullname: Zheng, Libin – sequence: 4 givenname: Han surname: Wu fullname: Wu, Han – sequence: 5 givenname: Peng surname: Cheng fullname: Cheng, Peng – sequence: 6 givenname: Lei surname: Chen fullname: Chen, Lei – sequence: 7 givenname: Xuemin surname: Lin fullname: Lin, Xuemin |
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Snippet | In this paper, we study the privacy-preserving task assignment in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release... |
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Title | Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server |
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