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A Task-based Personalized Privacy-Preserving Participant Selection Mechanism for Mobile Crowdsensing

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Abstract

Participant selection is one of the most crucial problems in mobile crowdsensing, which relies on the feature similarity between tasks and mobile users to select participants appropriately. However, since the server cannot be fully trustable, participant selection suffers some security and privacy challenges. Although there are many approaches to protect users’ privacy, the privacy of sensitive task requesters has been neglected. The untrusted server may infer private information such as the requester’s hobbies and location from the sensitive task. In addition, none of the existing privacy-preserving participant selection mechanisms can provide personalized protection considering diverse protection needs of users. In this paper, we propose a task-based personalized privacy protection participant selection mechanism that can securely select participants based on the attribute vector of the task while providing personalized privacy protection for participants. The basic idea is that the task requester and candidate users perturb their attribute vector and upload them to the server. The server performs similarity estimation on the obfuscated attribute vector to select high-quality participants. Furthermore, we propose a truth discovery algorithm for obfuscated values to improve the final result’s accuracy. We also theoretically analyze the privacy and utility of our propsoed mechanism, which outperforms the state-of-the-art solutions in both privacy protection degrees and time complexity. Experiments on synthetic datasets validate the effectiveness and efficiency of our mechanism.

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The data used to support the findings of this study are included within the article.

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Funding

This work was supported in part by the National Key R&D Program of China (Grant No. 2018YFB2100400), the Key Research and Development Program of Shaanxi (Grant No. 2019ZDLGY13-03-01, 2021ZDLGY07-05), the Innovation Capability Support Program of Shaanxi(Grant No. 2020CGXNG-002), and the Fundamental Research Funds for the Central Universities (Grant No. JB210306).

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, China

    Lele Zheng, Tao Zhang, Yulong Shen, Bowen Deng & Ze Tong

Authors
  1. Lele Zheng
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  2. Tao Zhang
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  3. Yulong Shen
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  4. Bowen Deng
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  5. Ze Tong
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Contributions

The data was analyzed and the manuscript was written by Zheng Lele. Zhang Tao was involved in the study’s conceptualization. Shen Yulong contributed to the analysis by providing helpful feedback. Deng Bowen made a substantial contribution to the study and paper preparation. The manuscript was edited by Tong Ze. The final manuscript was reviewed and approved by all all the authors.

Corresponding author

Correspondence to Tao Zhang.

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Ethics Approval

This paper concerns an expansion of previous work [21]. Building on previous work, we explore a task-based personalized privacy-preserving participant selection mechanism for mobile crowdsensing, which can securely select participating users based on the attribute vector of the task while providing personalized privacy protection for users. There are numerous major additions to this work as compared to the preliminary version: 1)During the aggregation process, we completely address the unique privacy protection requirements of distinct users and demonstrate the efficacy of our solution. 2) We run simulations to evaluate and compare our suggested solution to existing ones.

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There are no conflicts of interest declared by the authors.

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Zheng, L., Zhang, T., Shen, Y. et al. A Task-based Personalized Privacy-Preserving Participant Selection Mechanism for Mobile Crowdsensing. Mobile Netw Appl 28, 1647–1657 (2023). https://doi.org/10.1007/s11036-023-02100-2

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