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Register a new userA Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
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Added by
Teng Wang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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) has been 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 users 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. Whats more, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.
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Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Alas, recent work highlighted several privacy and robustness weaknesses in FL, presenting, respectively, membership/property inference and backdoor attacks. In this paper, we investigate to what extent Differential Privacy (DP) can be used to protect not only privacy but also robustness in FL. We present a first-of-its-kind empirical evaluation of Local and Central Differential Privacy (LDP/CDP) techniques in FL, assessing their feasibility and effectiveness. We show that both DP variants do defend against backdoor attacks, with varying levels of protection and utility, and overall much more effectively than previously proposed defenses. They also mitigate white-box membership inference attacks in FL, and our work is the first to show how effectively; neither, however, provides viable defenses against property inference. Our work also provides a re-usable measurement framework to quantify the trade-offs between robustness/privacy and utility in differentially private FL.
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