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

1
School of Cyberspace Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(24), 7030; https://doi.org/10.3390/s20247030
Received: 19 October 2020 / Revised: 19 November 2020 / Accepted: 3 December 2020 / Published: 8 December 2020
(This article belongs to the Special Issue Data Security and Privacy in the IoT)
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. View Full-Text
Keywords: local differential privacy; data statistics and analysis; enabling mechanisms; applications local differential privacy; data statistics and analysis; enabling mechanisms; applications
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MDPI and ACS Style

Wang, T.; Zhang, X.; Feng, J.; Yang, X. A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis. Sensors 2020, 20, 7030. https://doi.org/10.3390/s20247030

AMA Style

Wang T, Zhang X, Feng J, Yang X. A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis. Sensors. 2020; 20(24):7030. https://doi.org/10.3390/s20247030

Chicago/Turabian Style

Wang, Teng, Xuefeng Zhang, Jingyu Feng, and Xinyu Yang. 2020. "A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis" Sensors 20, no. 24: 7030. https://doi.org/10.3390/s20247030

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