Dyadic distribution-based compression and encryption with adaptive transformation matrix generation

Abstract

An adaptive transformation matrix generation is used simultaneous compression and encryption of data. The system continuously monitors input data streams to detect changes in distribution patterns, dynamically updating transformation matrices in response to these changes. Using performance evaluation criteria, the system selects and deploys optimal matrices that maintain both compression efficiency and cryptographic security as data characteristics evolve. The system implements configurable adaptation policies that govern when and how matrices are updated, ensuring system stability while maximizing performance. By employing sliding window analysis and distribution variance metrics, the system can quantify data drift and trigger appropriate adaptations. The system maintains compatibility with various operational modes including lossless, lossy, and modified lossless configurations, applying mode-specific optimization criteria to each scenario. This adaptive approach offers a resilient solution for data transmission and storage scenarios where both data reduction and security must be maintained across diverse and evolving data streams.