Cloud computing is a significant model for permitting on-demand network access to shared data, software’s, infrastructure, and platform resources. However, cloud storage needs a certain level of availability, confidentiality, and integrity. Information sensitivity and value encourage users to select a highly secure protocol. This work proposes a new mechanism to increase the user trust in cloud computing using the secret sharing technique. The proposed algorithm is using Base64 encoding to convert any file type to ASCII strings. Base64 strings do not need any extra process to be compressed and this can speed up the share building process. Each string is used to produce N shares (using Shamir Secret Sharing Scheme) where each share is stored in a separate location in the cloud.

Underwater localization using airborne visible light beams offers a promising alternative to acoustic and radio-frequency methods, yet accurate modeling of light propagation through a dynamic air–water interface remains a major challenge. This paper introduces a physics-informed machine learning framework that combines geometric optics with neural network inference to localize submerged optical nodes under both flat and wavy surface conditions. The approach integrates ray-based light transmission modeling with a third-order Stokes wave formulation, enabling a realistic representation of nonlinear surface slopes and their effect on refraction. A multilayer perceptron (MLP) is trained on synthetic intensity–position datasets generated from this model, learning the complex mapping between received optical power (light intensity) and coordinates of the submerged receiver. The proposed method demonstrates high precision, stability, and adaptability across varying geometries and surface dynamics, offering a computationally efficient solution for optical localization in dynamic underwater environments.