A More Secure Spatial Decompositions Algorithm via Indefeasible Laplace Noise in Differential Privacy

Li, Xiaocui; Wang, Yangtao; Zhang, Xinyu; Zhou, Ke; Li, Chunhua

doi:10.1007/978-3-030-05090-0_19

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Each final cell is denoted by the region X i . Different from the existing spatial decompositions [30], such a partition method takes into account the distribution characteristics of urban roads and ensures the convexity and compactness of locations in each cell.…”

Section: B Domain Partitionmentioning

confidence: 99%

Geo-obfuscation Mechanisms for Spatial Crowdsourcing via Multi-Objective Evolutionary Optimization

Zhang¹,

Zhang²,

Chen³

et al. 2022

Preprint

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Industrial Internet of Things (IIoT) has exploded key revolutions in several leading industries, such as energy, agriculture, mining, transportation, and healthcare. Due to the nature of high capacity and fast transmission speed, 5G plays a pivot role in enhancing the industrial procedures, practices and guidelines, such as crowdsourcing, cloud outsourcing and platform subcontracting. Spatial crowdsourcing (SC)-servers (such as offered by DiDi, MeiTuan and Uber) assign different tasks based on workers' location information. However, SC-servers are often untrustworthy and have the threat of revealing workers' privacy. In this paper, we introduce a framework Geo-MOEA (Multi-Objective Evolutionary Algorithm) to protect location privacy of workers involved on SC platform in 5G environment. We propose an adaptive regionalized obfuscation mechanism with inference error bounds based on geo-indistinguishability (a strong notion of differential privacy), which is suitable for the context of large-scale location data and task allocations. This offers locally generated pseudo-locations of workers to be reported instead of their actual locations. Further, to optimize the trade-off between SC service availability and privacy protection, we utilize MOEA to improve the global applicability of the mechanism in 5G environment. Finally, by simulating the location scenario, the visual results on experiments show that the mechanism can not only protect location privacy, but also achieve high availability of services as desired.

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Section: B Domain Partitionmentioning

confidence: 99%

Geo-obfuscation Mechanisms for Spatial Crowdsourcing via Multi-Objective Evolutionary Optimization

Zhang¹,

Zhang²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A suitable PSD can often improve the success rate of task assignment and reduce system overhead. Previously, PSDs are usually based on tree, especially kd-tree and quadtree [3,17], and the result is a deep tree. The typically simple method should be Uniform Grid method (UG), which treats all dense and sparse regions equally in the domain [4].…”

Section: Related Workmentioning

confidence: 99%

A Differentially Private Framework for Spatial Crowdsourcing with Historical Data Learning

Zhang,

Duan,

Chen

et al. 2020

Preprint

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Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy. It requires workers to be physically present at a particular location for task fulfillment. Effective protection of location privacy is essential for workers' enthusiasm and valid task assignment. However, existing SC models with differential privacy protection usually deploy only real-time location data, and their partitioning and noise additions overlaps for grids generation. Such a way may produce large perturbations to counting queries that affect success rate of task assignment and the accuracy of allocation results. In this paper, we propose a privacy framework (R-HT) for protecting location data of workers involved in SC. We use historical data learning to perform partitioning, and import real-time data directly into the second-level grid with perturbation, which realizes parallel allocation of privacy budget and a more suitable Private Spatial Decomposition (PSD) approach. Moreover, as geocast region (GR) construction, we introduce some optimization strategies, including scoring function and adaptive selection of locally maximum geocast radius. A series of experimental results on real-world datasets shows that our proposed R-HT scheme attains a stable success rate of task assignment, saves obvious performance overhead and is also fit for dynamic assignment of tasks on online SC platforms.

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A More Secure Spatial Decompositions Algorithm via Indefeasible Laplace Noise in Differential Privacy

Cited by 2 publications

References 24 publications

Geo-obfuscation Mechanisms for Spatial Crowdsourcing via Multi-Objective Evolutionary Optimization

Geo-obfuscation Mechanisms for Spatial Crowdsourcing via Multi-Objective Evolutionary Optimization

A Differentially Private Framework for Spatial Crowdsourcing with Historical Data Learning

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