AI & ML
Design and Evaluation of Secure Distributed Chaotic Image Encryption on ESP32-S3
arXiv cs.CR0 views
arXiv:2611.10595v2 Announce Type: replace Abstract: The limited computational resources of Internet of Things (IoT) devices present challenges for secure image processing. This paper presents a distributed chaotic image encryption system using independent Wi-Fi-connected ESP32-S3 microcontrollers. A master-worker architecture distributes image blocks through master-coordinated chaotic sequence management, maintaining one logically continuous chaotic sequence by assigning block-specific starting states independently of worker assignments. The implemented system uses reversible chaotic substitution (SUBSTITUTE), while Elliptic Curve Digital Signature Algorithm (ECDSA)-signed discovery messages and Transport Layer Security (TLS) 1.3 with mutual authentication secure device communication. Experimental evaluation using one, two, and four workers demonstrates a reduction in execution time from 43.215 to 15.878 seconds, achieving a speed-up of 2.72 times. The encrypted image achieves a Shannon entropy of 7.999940 bits/byte and near-zero adjacent-pixel correlation. Exact image recovery is verified through matching Secure Hash Algorithm 256-bit (SHA-256) hashes and a Structural Similarity Index (SSIM) value of 1.0. Additional experiments demonstrate successful encryption and decryption using different worker counts and block sizes, automatic task reassignment following worker failure, and rejection of a mismatched private key during TLS authentication. These findings demonstrate the feasibility of secure, flexible, and distributed chaotic image processing across independent resource-constrained IoT devices.
Source:https://arxiv.org/abs/2611.10595PreviousMitigating Private Data Leakage in LLMs with WhiteoutNextRoCoMark: Robust Code Watermarking Against LLM-Based Functionality-Preserving Rewriting Attacks
Related posts
FDR-Controlled Variable Selection for Generalized Linear Models and Cox Regression with Virtual Dummies
0arXiv eess.SP
Terrain Perception for Agricultural UAVs in Complex Farmland via Rotating mmWave Radar
0arXiv eess.SP
FUTON: Fourier Tensor Networks for Implicit Neural Representations
0arXiv eess.SP
Writing Bug Reports for Software Repair Agents: What Information Matters Most?
0arXiv cs.SE
SACS: A Code Smell Dataset using Semi-automatic Generation Approach
0arXiv cs.SE
Resolution of Vinzant's Conjecture on Phase Retrieval Injectivity
0arXiv math.IT