Home⇛Scientific Augustinian⇛vol. 15 no. 1 (2025)

Chaotic Water Caustic Patterns as a Physical Entropy Source for Cryptographic Key Generation in Cloud Computing Security

Yohann Jed R. Alamon | Gegan Ray G. Cahilig | Ion Nathaniel F. De Paula | Russell N. Gorre | Kalvin Joy Bauno

Discipline: Pharmacology

 

Abstract:

The increasing volume of data stored and transmitted in cloud computing environments raises critical concerns regarding privacy, confidentiality, and cybersecurity. This study proposes an entropy-driven cryptographic key derivation framework that utilizes chaotic water caustic patterns as a natural physical entropy source. An experimental computational design was employed using two independent experimental setups involving varying image resolutions (320 × 240, 640 × 480, 800 × 600, 1280 × 720, and 1920 × 1080) and plaintext types (repetitive text, sequential text, natural language text, pseudo-random strings, and heterogeneous Unicode text). The proposed framework captures dynamic water caustic patterns through a camera-based acquisition system, converts the captured patterns into cryptographic seeds using SHA-256 hashing, and integrates the resulting entropy into a HMAC-based Key Derivation Function (HKDF) for implementation within a ChaCha20-Poly1305 authenticated encryption scheme. Results demonstrated consistently high randomness quality, with several experimental conditions achieving complete pass rates in the applied NISTSP 800-22 statistical randomness tests. No statistically significant differences were observed across image resolutions and plaintext types for key performance metrics, including known-plaintext attack resistance (~50%), differential rate (~50%), avalanche effect (~50%), and encryption time (~0.18–0.21 s). However, Shannon entropy differed significantly across image resolutions (H(4) = 41.307, p < 0.001), with post hoc analysis revealing selective pairwise differences. Similarly, chosen-plaintext attack (CPA) entropy significantly varied across plaintext types (F(4, 245) = 1300.176, p < 0.001), with all pairwise comparisons showing statistical significance, suggesting that ciphertext statistical characteristics may vary according to plaintext structure under the tested conditions. Overall, the findings support the preliminary feasibility of using chaotic water caustics as a physical entropy source for cryptographic key generation. Nevertheless, the results also highlight the need for further validation under real-world deployment conditions, larger-scale cloud computing environments, and more comprehensive adversarial testing frameworks.



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