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• W4328006290 abstract "Mobile devices and mobile applications benefit people's mobility. In some applications such as Uber and Didi, drivers and passengers (i.e., users) submit locations to the server to match each other. However, the server may be untrusted and location privacy becomes a huge concern. Users expect to enjoy matching while protecting their locations and different users have different levels of location privacy. In this paper, we consider the personalized privacy-preserving online minimum bipartite matching problem. To solve this problem, we design a Personalized Quadtree-Based privacy-preserving matching Framework (PQBF). In this framework, we propose a privacy method using the truncated Geometric Mechanism on the quadTree (GMT). GMT allows users to intuitively select generalization regions with different sizes, which is user-friendly. We then formulate the distance on the quadtree using earth mover's distance (EMD) to select the closest worker-task pair. Considering the huge time overhead of distance calculation, we provide a way to accelerate. We show that our method satisfies <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$epsilon$</tex> - geo-indistinguishability on the quadtree and give the time complexity of PQBF. Experiments on synthetic data and real data show that our mechanism is more effective than previous mechanisms in terms of total distance. PQBF saves up to 35.9% total distance than a state-of-the-art algorithm." @default.
• W4328006290 created "2023-03-22" @default.
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• W4328006290 date "2022-08-01" @default.
• W4328006290 modified "2023-10-16" @default.
• W4328006290 title "Personalized Differentially Private Online Minimum Bipartite Matching in Spatial Crowdsourcing" @default.
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• W4328006290 doi "https://doi.org/10.1109/bigcom57025.2022.00025" @default.
• W4328006290 hasPublicationYear "2022" @default.
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