Abstract

The rapid growth of digital communication cloud storage and interconnected smart devices has significantly elevated the risks associated with data leakage unauthorized access and cyber threats. As sensitive information increasingly exists in the form of binary data—from medical records and financial transactions to biometric datasets and digital identity systems—there is a pressing need to ensure secure encryption methods capable of withstanding adversarial attacks while maintaining efficiency. Locally Adaptive Data Coding (LADC) has emerged as an innovative technique for encrypting binary data by dynamically adjusting coding parameters based on local statistical variations within the data. Unlike traditional encryption tools that rely on static key operations or global transformation rules LADC makes it possible to convert binary sequences into coded versions that adaptively incorporate local entropy distribution enabling stronger concealment of structural patterns. This research pape