Section Article

Algorithm for Encrypting Confidential Binary Data Using Locally Adaptive Data Coding
Author(s): Pitambar Pandey

Abstract
Confidential binary data encryption has become one of the most sensitive and strategically important domains within contemporary computer science and information security. As digital ecosystems expand across interconnected networks cloud infrastructures distributed architectures and multi-device communication channels the volume of binary data requiring confidentiality assurance grows exponentially. Traditional encryption techniques provide structural protection but often lack adaptability to the dynamic statistical properties of real-time binary data streams. Locally adaptive data coding introduces a paradigm shift in encryption methodologies by incorporating region-specific statistical learning dynamic bit-pattern modelling local entropy assessment data-driven encoding transformations and intelligent context-sensitive encryption parameter selection. This paper develops a refined algorithmic framework for encrypting confidential binary data using locally adaptive coding focusing on how adaptive segmentation coding diversity local distribution modelling and variable transformation weights enhance security efficiency and structural robustness.