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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67216
Volume:
10 |
9 |
Sep 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science & Engineering (Data Science)
Sagar Institute of Research & Technology (SIRT), Bhopal
Computer Science & Engineering
Sagar Institute of Research & Technology (SIRT), Bhopal
The exponential growth of Internet of Things (IoT) deployments in digital governance, healthcare, and smart city surveillance has underscored the imperative need for robust visual data protection. Uncompressed digital images possess distinct intrinsic characteristics—such as massive data volumes, high pixel redundancy, and intense adjacent correlation—that render traditional text-based block ciphers computationally inefficient and vulnerable to cryptanalysis. This paper surveys contemporary lightweight image cryptosystems and proposes an advanced multi-tiered hybrid architecture tailored for edge-assisted IoT networks. By addressing the critical key distribution and latency limitations identified in existing chaotic models, the proposed framework decouples visual data security into distinct functional layers. A lightweight Convolutional Neural Network (CNN) at the edge extracts spatial features to shuffle pixel positions, destroying structural correlations dynamically. Subsequently, the RC6 variable-block symmetric cipher rapidly diffuses the scrambled payload using data-dependent rotations and integer multiplication, while an RSA asymmetric layer encapsulates the temporary session keys for secure transmission over public channels. Quantitative synthesis demonstrates that this decoupled architecture yields near-ideal Shannon entropy (≈ 7.997), completely flat intensity histograms, and exceptional resistance to statistical and differential attacks (NPCR ≈ 99.61%, UACI ≈ 33.46%), while delivering up to a 6.5-fold throughput improvement over conventional chaotic schemes.
KEYWORDS
Ensuring visual data privacy in IoT networks requires balancing strong cryptographic security with low computational latency. Through a comprehensive review of current literature, this study highlighted key vulnerabilities in existing chaotic and pure block cipher models, specifically their lack of secure key wrappers and high computational overhead on edge nodes. To resolve these limitations, we proposed a hybrid architecture combining CNN-based spatial shuffling, RC6 payload diffusion, and RSA asymmetric key encapsulation. Decoupling pixel confusion from payload diffusion bypasses the latency bottlenecks of classical ciphers, delivering up to a 6.5-fold throughput gain. Furthermore, RSA key encapsulation guarantees secure session key exchange across public cloud networks. This framework provides an optimized, scalable model for next-generation digital governance and IoT surveillance applications.
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Publication Date
9/12/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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