A survey of local differential privacy for securing internet of vehicles

Zhao, Ping; Zhang, Guanglin; Wan, Shaohua; Liu, Gaoyang; Umer, Tariq

doi:10.1007/s11227-019-03104-0

Cited by 45 publications

(24 citation statements)

References 72 publications

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“…Li and Ye [14] performed a small survey on LDP in a seminar talk that introduced its fundamental behind these practical deployment systems, reviewed its current research landscape, and recognized some open challenges in LDP. In addition, a recent review work [15] focused on the survey on LDP for securing Internet of vehicles, and it is different from the above-mentioned review studies since it is a survey of LDPbased application. Nevertheless, these studies of survey literature did not conduct a comprehensive and detailed survey on local differential privacy and, there does not exist a comprehensive and systematic survey on the existing research studies of LDP.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

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With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

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“…And several techniques have been proposed to protect privacy of data, such as k-anonymity, randomization and cryptographic tools [17]. Differential privacy is the first privacy protection model with rigorous and provable mathematical definition [18]. It has been widely used to design privacy-preserving schemes for data mining such as decision tree [19], principal component analysis [20], and artificial neural network [21].…”

Section: Related Workmentioning

confidence: 99%

A differentially private distributed data mining scheme with high efficiency for edge computing

Sun

et al. 2021

J Cloud Comp

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A wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.

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“…Zhao et al [ 176 ] adopted both LDP and FL models to avoid sensitive information leakage in IoV applications. Besides, the work in [ 39 ] has provided a detailed summary of the applications of LDP in the Internet of connected vehicles.…”

Section: Applicationsmentioning

confidence: 99%

“…Zhao et al. [ 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.…”

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 survey of local differential privacy for securing internet of vehicles

Cited by 45 publications

References 72 publications

A Comprehensive Survey on Local Differential Privacy

A Comprehensive Survey on Local Differential Privacy

A differentially private distributed data mining scheme with high efficiency for edge computing

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

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