A Comprehensive Survey on Local Differential Privacy

Xiong, Xingxing; Liu, Shubo; Li, Dan; Cai, Zheren; Niu, Xiaoguang

doi:10.1155/2020/8829523

Cited by 66 publications

(43 citation statements)

References 93 publications

(146 reference statements)

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“…Leveraging the quadtree indexing and geocoding, we propose QLP and QJLP algorithms to generate the perturbed location and trajectory data. The proposed algorithms are universal for all LDP mechanisms based on RR, such as Basic Rappor, OUE, OLH, O-Rappor, and K-RR [24]. Due to space constraints, only the Basic Rappor mechanism is adopted in RR steps (S1-3 and S2-5) of QLP and QJLP algorithms proposed in the paper.…”

Section: Qlp and Qjlp Algorithmsmentioning

confidence: 99%

Local Trajectory Privacy Protection in 5G Enabled Industrial Intelligent Logistics

Yang

Wang

et al. 2022

IEEE Trans. Ind. Inf.

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The value of trajectory data lies mainly in the spatiotemporal correlation. However, the existing privacy protection methods ignore the spatio-temporal correlation of trajectory data, resulting in a large error in trajectory proportion estimation and Top-K classification. For the privacy of truck trajectory in intelligent logistics, the location and trajectory data perturbation method based on quadtree indexing is proposed, which leverages location generalization and local differential privacy techniques. Our proposed algorithms are suitable for datasets with a large sample space and can protect the trajectory privacy of truck drivers while preserving the strong correlation between adjacent spatio-temporal nodes in the trajectory. The results of simulation on a real trajectory dataset show that the proposed methods not only meet the trajectory privacy requirements of users but also have a good performance in trajectory proportion estimation and Top-K classification.Index Terms-Intelligent logistics; trajectory privacy; spatiotemporal correlation; local differential privacy; quadtree indexing I. INTRODUCTION I NTELLIGENT logistics is an important application of the Industrial Internet of Things (IIoT) and a key part of the whole IIoT system. The development of intelligent logistics is inseparable from the support of communication techniques. The data transmission among the logistics nodes such as trucks, warehouses, workers, and target customers requires high quality communication services, the supporting technology for the evolution of intelligent logistics, such as Internet of Things, cloud computing, big data analytics, artificial intelligence [1], etc., and relies on the communication infrastructure with large capacity, high broadband, low delay, and high reliability. At present, the 5G wireless communications with the characteristics of ultra-high bandwidth, wide

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Section: Qlp and Qjlp Algorithmsmentioning

confidence: 99%

Local Trajectory Privacy Protection in 5G Enabled Industrial Intelligent Logistics

Yang

Wang

et al. 2022

IEEE Trans. Ind. Inf.

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“…The local DP model has received considerable attention in both academia [4,15,19,25,[32][33][34] and practical deployment [9,13] since it does not rely on sharing raw data anymore, which has a clear connection to the concept of randomized response [35]. We refer the interested reader to the survey work on LDP from Xiong et al [37] for more insights about this approach.…”

Section: Related Workmentioning

confidence: 99%

“…There are few works for collecting multi-dimensional data with LDP based on random sampling [25,32,33], which mainly focused on numerical data [37]. This technique reduces both dimensionality and communication costs, which will also be the focus of this paper by extending the analysis to the GRR mechanism.…”

Section: Collecting Multi-dimensional Data With Grrmentioning

confidence: 99%

Longitudinal Collection and Analysis of Mobile Phone Data with Local Differential Privacy

Arcolezi¹,

Couchot²,

Bouna³

et al. 2021

IFIP Advances in Information and Communication Technology

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Longitudinal studies of human mobility could allow an understanding of human behavior on a vast scale. Mobile phone data call detail records (CDRs) have emerged as a prospective data source for such an important task. Nevertheless, there are significant risks when it comes to collecting this type of data, as human mobility has proven to be quite unique. Because CDRs are produced through the connection of mobile phones with mobile network operators' (MNOs) antennas, it means that users cannot sanitize their data. Once MNOs intend to use such a data source for human mobility analysis, data protection authorities such as the CNIL (in France) recommends that data be sanitized on the fly instead of collecting raw data and publishing private output at the end of the analysis. Local differential privacy (LDP) mechanisms are currently applied during the process of data collection to preserve the privacy of users. In this paper, we propose an efficient privacy-preserving LDP-based methodology to collect and analyze multi-dimensional data longitudinally through mobile connections. In our proposal, rather than regarding users as unique IDs, we propose a generic scenario where one directly collects users' sensitive data with LDP. The intuition behind this is collecting generic values, which can be generated by many users (e.g., gender), allowing a longitudinal study. As we show in the results, our methodology is very appropriate for this scenario, achieving accurate frequency estimation in a multi-dimensional setting while respecting some major recommendations of data protection authorities such as the GDPR and CNIL. This work was supported by the Region of Bourgogne Franche-Comté CADRAN Project and by the EIPHI-BFC Graduate School (contract "ANR-17-EURE-0002"). The authors would also like to thank the Orange Application for Business team for their useful feedback and comments. Computations have been performed on the supercomputer facilities of "Mésocentre de Calcul de Franche-Comté".

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“…[ 39 ] reviewed the existing LDP-based mechanisms only towards the Internet of connected vehicles. The reviews in [ 40 , 41 ] also provided a survey of statistical query and private learning with LDP. However, the detailed technical points and specific data types when using LDP are still insufficiently summarized.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang

Zhang

Feng

et al. 2020

Sensors

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Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

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A Comprehensive Survey on Local Differential Privacy

Cited by 66 publications

References 93 publications

Local Trajectory Privacy Protection in 5G Enabled Industrial Intelligent Logistics

Local Trajectory Privacy Protection in 5G Enabled Industrial Intelligent Logistics

Longitudinal Collection and Analysis of Mobile Phone Data with Local Differential Privacy

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

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