A Blockchain-Based Secure Image Encryption Scheme for the Industrial Internet of Things
Open accessAbstract
Smart cameras and image sensors are widely used in industrial processes, from the designing to the quality checking of the final product. Images generated by these sensors are at continuous risk of disclosure and privacy breach in the industrial Internet of Things (IIoT). Traditional solutions to secure sensitive data fade in IIoT environments because of the involvement of third parties. Blockchain technology is the modern-day solution for trust issues and eliminating or minimizing the role of the third party. In the context of the IIoT, we propose a permissioned private blockchain-based solution to secure the image while encrypting it. In this scheme, the cryptographic pixel values of an image are stored on the blockchain, ensuring the privacy and security of the image data. Based on the number of pixels change rate (NPCR), the unified averaged changed intensity (UACI), and information entropy analysis, we evaluate the strength of proposed image encryption algorithm ciphers with respect to differential attacks. We obtained entropy values near to an ideal value of 8, which is considered to be safe from brute force attack. Encrypted results show that the proposed scheme is highly effective for data leakage prevention and security.
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The entropy results generated by SCEC when applied to a series of well-known images surpassed the performance of other cryptosystems.
“The entropy results generated by SCEC when applied to a series of well-known images surpassed the performance of other cryptosystems [30], [31].”
From Cryptosystem Based on the Elliptic Curve With a High Degree of Resistance to Damage on the Encrypted Images (2020) · cites this paper as [30]
Section 5.4 (Entropy Analysis; page unavailable) reports near-ideal entropy values for Lena and Cameraman and says it compares them with existing techniques. The passage does not identify the scheme as SCEC or establish that its entropy results surpassed other cryptosystems. (LLM, conf=0.82)
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- Authors
- Khan, P. W., & Byun, Y.
- Affiliations
- Jeju National University (KR)
- Department of Computer Engineering, Jeju National University, Jeju-si 690-011, Korea
- Venue
- Entropy
- Publisher
- MDPI AG
- Published
- 2020-02-04
- Volume
- 22
- Issue
- 2
- Pages
- 175
- ISSN
- 1099-4300 (ISSN-L)
- PMC
- PMC7516591
- OpenAlex
- W3005115714
- Cited by
- 137
- References
- 55
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 26 · 2023: 26 · 2022: 26 · 2021: 28 · 2020: 11
- FWCI
- 8.84
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Entropy (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Ministry of Trade, Industry and Energy (N0002327)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptographic Implementations and Security
- Keywords
- image encryption, blockchain, Industrial Internet of Things, data privacy, data loss prevention, smart cameras, image sensors, NPCR, UACI, Shannon entropy, differential attack resistance
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.
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