Abstract
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.
Highlights
With the rapid development of wireless communication techniques, Internet-connected devices are ever-increasing and generate large amounts of data by crowdsensing [1]
It is still necessary and urgent to carry out a comprehensive survey on Local differential privacy (LDP) toward data statistics and analysis to help newcomers understand the complex discipline of this hot research area
We explore the practical applications with LDP to show how LDP is to be implemented in various applications, including in real systems (e.g., Google Chrome, Apple iOS), edge computing, hypothesis testing, social networks, and recommendation systems
Summary
With the rapid development of wireless communication techniques, Internet-connected devices (e.g., smart devices and IoT appliances) are ever-increasing and generate large amounts of data by crowdsensing [1]. It is still necessary and urgent to carry out a comprehensive survey on LDP toward data statistics and analysis to help newcomers understand the complex discipline of this hot research area. In this survey, we conduct an in-depth overview of LDP with respect to its privacy models, the related research tasks for various data, enabling mechanisms, and wide applications. From the perspective research tasks, we summarize the existing LDP-based privacy-preserving mechanisms into three categories: frequency estimation , mean estimation and machine learning.
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