GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel
The existing work has conducted in-depth research and analysis on global differential privacy (GDP) and local differential privacy (LDP) based on information theory. However, the data privacy preserving community does not systematically review and analyze GDP and LDP based on the information-theoret...
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MDPI AG
2022-03-01
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Online Access: | https://www.mdpi.com/1099-4300/24/3/430 |
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author | Hai Liu Changgen Peng Youliang Tian Shigong Long Feng Tian Zhenqiang Wu |
author_facet | Hai Liu Changgen Peng Youliang Tian Shigong Long Feng Tian Zhenqiang Wu |
author_sort | Hai Liu |
collection | DOAJ |
description | The existing work has conducted in-depth research and analysis on global differential privacy (GDP) and local differential privacy (LDP) based on information theory. However, the data privacy preserving community does not systematically review and analyze GDP and LDP based on the information-theoretic channel model. To this end, we systematically reviewed GDP and LDP from the perspective of the information-theoretic channel in this survey. First, we presented the privacy threat model under information-theoretic channel. Second, we described and compared the information-theoretic channel models of GDP and LDP. Third, we summarized and analyzed definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP under their channel models. Finally, we discussed the open problems of GDP and LDP based on different types of information-theoretic channel models according to the above systematic review. Our main contribution provides a systematic survey of channel models, definitions, privacy-utility metrics, properties, and mechanisms for GDP and LDP from the perspective of information-theoretic channel and surveys the differential privacy synthetic data generation application using generative adversarial network and federated learning, respectively. Our work is helpful for systematically understanding the privacy threat model, definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP from the perspective of information-theoretic channel and promotes in-depth research and analysis of GDP and LDP based on different types of information-theoretic channel models. |
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institution | Directory Open Access Journal |
issn | 1099-4300 |
language | English |
last_indexed | 2024-03-09T13:42:57Z |
publishDate | 2022-03-01 |
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series | Entropy |
spelling | doaj.art-1cb7ae2646aa46728501da5a5e40d8362023-11-30T21:04:20ZengMDPI AGEntropy1099-43002022-03-0124343010.3390/e24030430GDP vs. LDP: A Survey from the Perspective of Information-Theoretic ChannelHai Liu0Changgen Peng1Youliang Tian2Shigong Long3Feng Tian4Zhenqiang Wu5Guizhou Big Data Academy, Guizhou University, Guiyang 550025, ChinaGuizhou Big Data Academy, Guizhou University, Guiyang 550025, ChinaCollege of Computer Science and Technology, Guizhou University, Guiyang 550025, ChinaCollege of Computer Science and Technology, Guizhou University, Guiyang 550025, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaThe existing work has conducted in-depth research and analysis on global differential privacy (GDP) and local differential privacy (LDP) based on information theory. However, the data privacy preserving community does not systematically review and analyze GDP and LDP based on the information-theoretic channel model. To this end, we systematically reviewed GDP and LDP from the perspective of the information-theoretic channel in this survey. First, we presented the privacy threat model under information-theoretic channel. Second, we described and compared the information-theoretic channel models of GDP and LDP. Third, we summarized and analyzed definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP under their channel models. Finally, we discussed the open problems of GDP and LDP based on different types of information-theoretic channel models according to the above systematic review. Our main contribution provides a systematic survey of channel models, definitions, privacy-utility metrics, properties, and mechanisms for GDP and LDP from the perspective of information-theoretic channel and surveys the differential privacy synthetic data generation application using generative adversarial network and federated learning, respectively. Our work is helpful for systematically understanding the privacy threat model, definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP from the perspective of information-theoretic channel and promotes in-depth research and analysis of GDP and LDP based on different types of information-theoretic channel models.https://www.mdpi.com/1099-4300/24/3/430GDP vs. LDPinformation-theoretic channelRényi divergencemutual informationexpected distortion |
spellingShingle | Hai Liu Changgen Peng Youliang Tian Shigong Long Feng Tian Zhenqiang Wu GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel Entropy GDP vs. LDP information-theoretic channel Rényi divergence mutual information expected distortion |
title | GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel |
title_full | GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel |
title_fullStr | GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel |
title_full_unstemmed | GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel |
title_short | GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel |
title_sort | gdp vs ldp a survey from the perspective of information theoretic channel |
topic | GDP vs. LDP information-theoretic channel Rényi divergence mutual information expected distortion |
url | https://www.mdpi.com/1099-4300/24/3/430 |
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