Area coverage-based worker recruitment under geo-indistinguishability

Zhang, Pengfei; Chen, Xiang; Sen, Sandip; Wang, Ning

doi:10.1016/j.comnet.2022.109340

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Geo-I has gained significant attention in various LBS applications due to its ability to protect location privacy. In mobile crowdsourcing, where workers must share their locations with mobile crowdsourcing servers to allocate sensing tasks to the nearest workers, Geo-I is employed to obfuscate workers' true locations, thereby protecting their location privacy [26][27][28][29]. Location-based social networking platforms assist people in connecting with each other, but they also pose a threat to location privacy.…”

Section: Geo-indistinguishability and Its Applicationsmentioning

confidence: 99%

“…In this paper, we introduce a novel perturbation mechanism to achieve -Geo-I that can be efficiently applied to vectors represented in a high-dimensional space. The proposed method is inspired by the perturbation mechanism in [29]. However, unlike the mechanism in [29], which does not use prior data distribution, our approach utilizes a prior data distribution to generate perturbed data with higher utility, similar to the optimization mechanism.…”

Section: Computation Of Obfuscation Matrixmentioning

confidence: 99%

“…The proposed method is inspired by the perturbation mechanism in [29]. However, unlike the mechanism in [29], which does not use prior data distribution, our approach utilizes a prior data distribution to generate perturbed data with higher utility, similar to the optimization mechanism. Given a set of all medical microdata, S = {s 1 , s 2 , • • • , s m }, that the data aggregator aims to collect from users, let V = {v 1 , v 2 , • • • , v m } represent the set of corresponding vectors obtained using Word2Vec in the previous phase.…”

Section: Computation Of Obfuscation Matrixmentioning

confidence: 99%

“…The approach that utilizes the perturbation mechanism proposed in [29], which does not use a prior distribution (NP); •…”

mentioning

confidence: 99%

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Adapting Geo-Indistinguishability for Privacy-Preserving Collection of Medical Microdata

Song

Kim

2023

Electronics

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In the era of the Fourth Industrial Revolution, the increasing demand for data collection and sharing for analysis purposes has raised concerns regarding privacy violations. Protecting individual privacy during the collection and dissemination of sensitive information has emerged as a critical concern. In this paper, we propose a privacy-preserving framework for collecting users’ medical microdata, utilizing geo-indistinguishability (Geo-I), a concept based on well-known differential privacy. We adapt Geo-I, originally designed for protecting location information privacy, to collect medical microdata while minimizing the reduction in data utility. To mitigate the reduction in data utility caused by the perturbation mechanism of Geo-I, we propose a novel data perturbation technique that utilizes the prior distribution information of the data being collected. The proposed framework enables the collection of perturbed microdata with a distribution similar to that of the original dataset, even in scenarios that demand high levels of privacy protection, typically requiring significant perturbations to the original data. We evaluate the performance of our proposed algorithms using real-world data and demonstrate that our approach significantly outperforms existing methods, ensuring user privacy while preserving data utility in medical data collection.

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Section: Geo-indistinguishability and Its Applicationsmentioning

confidence: 99%

Section: Computation Of Obfuscation Matrixmentioning

confidence: 99%