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

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. Lo...

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Published inSensors (Basel, Switzerland) Vol. 20; no. 24; p. 7030
Main Authors Wang, Teng, Zhang, Xuefeng, Feng, Jingyu, Yang, Xinyu
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 08.12.2020
MDPI
Subjects
Algorithms
applications
Big Data
data statistics and analysis
enabling mechanisms
Hypothesis testing
Internet of Things
local differential privacy
Machine learning
Privacy
Probability
Recommender systems
Review
enabling mechanisms
data statistics and analysis
local differential privacy
applications
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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.
AbstractList 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.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.
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.
Author Feng, Jingyu
Yang, Xinyu
Wang, Teng
Zhang, Xuefeng
AuthorAffiliation 2 School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China; yxyphd@mail.xjtu.edu.cn
1 School of Cyberspace Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China; zhangxuefeng3@163.com (X.Z.); fengjy@xupt.edu.cn (J.F.)
AuthorAffiliation_xml – name: 2 School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China; yxyphd@mail.xjtu.edu.cn
– name: 1 School of Cyberspace Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China; zhangxuefeng3@163.com (X.Z.); fengjy@xupt.edu.cn (J.F.)
Author_xml – sequence: 1
  givenname: Teng
  orcidid: 0000-0003-3067-4674
  surname: Wang
  fullname: Wang, Teng
– sequence: 2
  givenname: Xuefeng
  orcidid: 0000-0001-6056-667X
  surname: Zhang
  fullname: Zhang, Xuefeng
– sequence: 3
  givenname: Jingyu
  surname: Feng
  fullname: Feng, Jingyu
– sequence: 4
  givenname: Xinyu
  surname: Yang
  fullname: Yang, Xinyu
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33302517$$D View this record in MEDLINE/PubMed
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data statistics and analysis
local differential privacy
applications
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Snippet Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for...
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SubjectTerms Algorithms
applications
Big Data
data statistics and analysis
enabling mechanisms
Hypothesis testing
Internet of Things
local differential privacy
Machine learning
Privacy
Probability
Recommender systems
Review
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Title A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis
URI https://www.ncbi.nlm.nih.gov/pubmed/33302517
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https://www.proquest.com/docview/2470626636
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