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• W4360923424 abstract "Content-based task allocation is an important part of MCS, which can effectively help data requesters find suitable sensing users to collect data. However, users’ task information may reveal sensitive information, e.g., location and hobbies. An intuitive scheme is to encrypt task information and perform task queries in ciphertexts. However, considering that MCS has the characteristics of a large scale of users, many types of tasks, and resource-limited devices, schemes based on traditional time-consuming cryptographic tools will consume tremendous computing resources of the mobile crowdsensing platform. In order to solve the challenges, this chapter firstly proposes a basic privacy-preserving content-based task allocation scheme, named PPTA, based on random matrix multiplication technology and polynomial functions, which can achieve efficient task queries without revealing task privacy. In addition, this chapter extends PPTA so that it can efficiently support more functionalities in task allocation, such as conjunctive task allocation, Top-z task allocation, task allocation with access control, and task recovery. Through detailed security analysis, we demonstrate that PPTA can well preserve task privacy and query privacy. Experiments based on real mobile crowdsensing applications demonstrate that PPTA can achieve high data encryption and task query efficiency." @default.
• W4360923424 created "2023-03-26" @default.
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• W4360923424 date "2022-12-21" @default.
• W4360923424 modified "2023-09-28" @default.
• W4360923424 title "Privacy-Preserving Content-Based Task Allocation" @default.
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• W4360923424 doi "https://doi.org/10.1007/978-981-19-8315-3_3" @default.
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