Internet of Things networks are proliferating rapidly, and securing document sharing over the cloud presents a significant challenge. Traditional encryption techniques cannot create a compromise between security, efficiency, and scalability. The known encryption techniques such as AES, RSA, and ABE have high computational overheads and their inefficient key management render them unsuitable for larger-scale IoT environments. Homomorphic encryption and security models based on blockchain are said to deliver better security, but they come with high storage and processing costs along with latency concerns. Moreover, end-to-end encryption is lacking in cloud-based systems leading to data breach vulnerabilities, whereas machine-learning-based algorithms for anomaly detection do not readily adapt in real time and are susceptible to adversarial attacks. This research proposes in detail a Secure Multi-Party Computation (SMPC) framework capable of Private Set Intersection (PSI) by integrating homomorphic encryption with new cryptographic optimization techniques, thereby improving secure document exchange in cloud-based IoT environments. The research also applies Gaussian Walk Group Search Optimization, Adaptive Differential Evolution, and Anisotropic Random Walks (ARW) to optimize cryptographic key generation for privacy preserving data sharing. Besides, security and efficiency will be fortified through dynamic load balancing based on Infinite Gaussian Mixture Models (IGMM) and PLONK-based Zero-Knowledge Proofs. The experimental results demonstrate improved encryption strength, computational efficiency, accurate document matching, and low overhead as compared to traditional cryptographic models. The presented solution attains 92% on cloud efficiency, optimized speed in encryption and decryption, and security in the document-sharing platform. This makes the proposed framework very viable for securing IoT-based cloud applications.
