Abstract
At present, the data information is uploaded to the platform by a large number of users in mobile crowdsensing, which can be processed in the sensing platform. As a result, the sensing platform not only a target for attackers, but also greatly increases time delay and high bandwidth costs. To solve this problem, a user clustering privacy-preserving scheme based on edge servers (UPPE) is proposed. Firstly, the task requester sends the tasks to the platform, publishes the task after sensing the platform’s scalar task attributes, and the sensing user uploads the user task request to the edge server. Then, the edge server utilizes the clustering method to hide the information related users, taking the edge server as a clustering to sign the users and upload it to the platform. Finally, the platform randomly matches the users and selects the user set in the clustering. The edge server notifies the user to upload the sensing data after signature verification. The simulation results show that the proposed mechanism verifies the user availability to upload data and protect the identity of user.
This work was supported by the National Key Research and Development Project of China under Grant 2018YFB2100200.
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Yang, P., Zhang, Y., Wu, Q., Wu, D., Li, Z. (2020). User Grouping Privacy-Preserving Strategy Based on Edge Computing for Mobile Crowdsensing. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_50
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DOI: https://doi.org/10.1007/978-981-15-9129-7_50
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