Data security and privacy-preserving in edge computing paradigm: survey and open issues
Open accessAbstract
With the explosive growth of Internet of Things devices and massive data produced at the edge of the network, the traditional centralized cloud computing model has come to a bottleneck due to the bandwidth limitation and resources constraint. Therefore, edge computing, which enables storing and processing data at the edge of the network, has emerged as a promising technology in recent years. However, the unique features of edge computing, such as content perception, real-time computing, and parallel processing, has also introduced several new challenges in the field of data security and privacy-preserving, which are also the key concerns of the other prevailing computing paradigms, such as cloud computing, mobile cloud computing, and fog computing. Despites its importance, there still lacks a survey on the recent research advance of data security and privacy-preserving in the field of edge computing. In this paper, we present a comprehensive analysis of the data security and privacy threats, protection technologies, and countermeasures inherent in edge computing. Specifically, we first make an overview of edge computing, including forming factors, definition, architecture, and several essential applications. Next, a detailed analysis of data security and privacy requirements, challenges, and mechanisms in edge computing are presented. Then, the cryptography-based technologies for solving data security and privacy issues are summarized. The state-of-the-art data security and privacy solutions in edge-related paradigms are also surveyed. Finally, we propose several open research directions of data security in the field of edge computing.
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The security of smart contracts, regarded as core software on blockchain, is important because a small bug or attack can result in significant issues such as privacy leakage or system logic modifications.
“In addition, the security aspect of smart contract, which is regarded as core software on blockchain, is also very important since a small bug or attack can result in significant issues like privacy leakage or system logic modifications [297], [298].”
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- Authors
- Zhang, J., Chen, B., Zhao, Y., Cheng, X., & Hu, F.
- Affiliations
- Nanjing University of Aeronautics and Astronautics (CN)
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 18209-18237
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2794596638
- Cited by
- 514
- References
- 145
- Citations by year
- 2026: 22 · 2025: 82 · 2024: 72 · 2023: 56 · 2022: 86 · 2021: 86 · 2020: 63 · 2019: 40 · 2018: 7
- FWCI
- 30.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2017YFB0802303)
- National Natural Science Foundation of China (61672283, 61602238)
- Natural Science Foundation of Jiangsu Province (BK20160805)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- edge computing, data security, privacy preservation, Internet of Things, cryptography, privacy risks, security countermeasures, fog computing, mobile cloud computing, edge computing architecture
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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