Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map Article Swipe
K. Priyadarsini
,
Arun Kumar Sivaraman
,
Abdul Quadir
,
Areej Malibari
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.030648
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.030648
With the rapid evolution of Internet technology, fog computing has taken a major role in managing large amounts of data. The major concerns in this domain are security and privacy. Therefore, attaining a reliable level of confidentiality in the fog computing environment is a pivotal task. Among different types of data stored in the fog, the 3D point and mesh fog data are increasingly popular in recent days, due to the growth of 3D modelling and 3D printing technologies. Hence, in this research, we propose a novel scheme for preserving the privacy of 3D point and mesh fog data. Chaotic Cat map-based data encryption is a recently trending research area due to its unique properties like pseudo-randomness, deterministic nature, sensitivity to initial conditions, ergodicity, etc. To boost encryption efficiency significantly, in this work, we propose a novel Chaotic Cat map. The sequence generated by this map is used to transform the coordinates of the fog data. The improved range of the proposed map is depicted using bifurcation analysis. The quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate entropy. We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack. The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.
Related Topics
Concepts
Computer science
Chaotic
Encryption
Point cloud
Lyapunov exponent
Data mining
Randomness
Algorithm
Entropy (arrow of time)
Cloud computing
Chaotic map
Theoretical computer science
Distributed computing
Real-time computing
Computer security
Artificial intelligence
Mathematics
Operating system
Statistics
Physics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2023.030648
- https://file.techscience.com/files/cmc/2023/TSP_CMC-74-3/TSP_CMC_30648/TSP_CMC_30648.pdf
- OA Status
- diamond
- Cited By
- 5
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313329260
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313329260Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/cmc.2023.030648Digital Object Identifier
- Title
-
Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat MapWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-29Full publication date if available
- Authors
-
K. Priyadarsini, Arun Kumar Sivaraman, Abdul Quadir, Areej MalibariList of authors in order
- Landing page
-
https://doi.org/10.32604/cmc.2023.030648Publisher landing page
- PDF URL
-
https://file.techscience.com/files/cmc/2023/TSP_CMC-74-3/TSP_CMC_30648/TSP_CMC_30648.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://file.techscience.com/files/cmc/2023/TSP_CMC-74-3/TSP_CMC_30648/TSP_CMC_30648.pdfDirect OA link when available
- Concepts
-
Computer science, Chaotic, Encryption, Point cloud, Lyapunov exponent, Data mining, Randomness, Algorithm, Entropy (arrow of time), Cloud computing, Chaotic map, Theoretical computer science, Distributed computing, Real-time computing, Computer security, Artificial intelligence, Mathematics, Operating system, Statistics, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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