survey

Scenario-based Adaptations of Differential Privacy: A Technical Survey

Authors:

Jinjun ChenAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 8

Article No.: 199, Pages 1 - 39

https://doi.org/10.1145/3651153

Published: 26 April 2024 Publication History

Abstract

Differential privacy has been a de facto privacy standard in defining privacy and handling privacy preservation. It has had great success in scenarios of local data privacy and statistical dataset privacy. As a primitive definition, standard differential privacy has been adapted to a wide range of practical scenarios. In this work, we summarize differential privacy adaptations in specific scenarios and analyze the correlations between data characteristics and differential privacy design. We mainly present them in two lines including differential privacy adaptations in local data privacy and differential privacy adaptations in statistical dataset privacy. With a focus on differential privacy design, this survey targets providing guiding rules in differential privacy design for scenarios, together with identifying potential opportunities to adaptively apply differential privacy in more emerging technologies and further improve differential privacy itself with the assistance of cryptographic primitives.

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Cited By

Ji JZhao RWang TZhu Y(2024)Utility-Enhanced Image Obfuscation With Block Differential PrivacyIEEE Signal Processing Letters10.1109/LSP.2024.344571631(2135-2139)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3445716
Xu YZhang NWang AFeng TDu G(2024)Hospital readmission prediction with hybrid‐sampling and self‐paced balance learningConcurrency and Computation: Practice and Experience10.1002/cpe.815536:18Online publication date: 26-May-2024
https://doi.org/10.1002/cpe.8155

Index Terms

Scenario-based Adaptations of Differential Privacy: A Technical Survey
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 8

August 2024

963 pages

EISSN:1557-7341

DOI:10.1145/3613627

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
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Michela Milano
University of Bologna, Italy

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Publication History

Published: 26 April 2024

Online AM: 05 March 2024

Accepted: 15 December 2023

Revised: 29 May 2023

Received: 16 April 2022

Published in CSUR Volume 56, Issue 8

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Ji JZhao RWang TZhu Y(2024)Utility-Enhanced Image Obfuscation With Block Differential PrivacyIEEE Signal Processing Letters10.1109/LSP.2024.344571631(2135-2139)Online publication date: 2024
https://doi.org/10.1109/LSP.2024.3445716
Xu YZhang NWang AFeng TDu G(2024)Hospital readmission prediction with hybrid‐sampling and self‐paced balance learningConcurrency and Computation: Practice and Experience10.1002/cpe.815536:18Online publication date: 26-May-2024
https://doi.org/10.1002/cpe.8155

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