Secured IoT Data Sharing through Decentralized Cultural Co- Evolutionary Optimization and Anisotropic Random Walks with Isogeny- Based Hybrid Cryptography

Abstract

Background information: Data sharing has been transformed by the Internet of Things'
explosive expansion, yet security and privacy threats have increased. Methods: For safe IoT
data sharing, this study combines isogeny-based hybrid cryptography with anisotropic random
walks (ARW) and decentralised cultural co-evolutionary optimisation (DCCO). Our goals are
to develop a safe model for IoT data sharing, optimise it using DCCO, and improve the security
of data transfers. Results: With 97% overall accuracy and 96% data secrecy, the suggested
approach performed better than conventional techniques. Conclusion: By maximising
robustness and performance, this innovative method improves IoT security. Keywords: data
security, random walks, cryptography, IoT, and co-evolutionary optimisation.
Methods: DCCO, ARW, and isogeny-based cryptography are all combined in this study.
While ARW improves data transmission security by offering multidimensional randomisation,
DCCO dynamically adjusts security procedures. For data encryption, post-quantum security is
guaranteed using isogeny-based cryptography.
Objectives: Create a framework for decentralised IoT data security. Use DCCO to maximise
data-sharing flexibility. To ensure safe data transport, use ARW. Use post-quantum
cryptography techniques to improve the security of encryption.