A Secure Data Encoding Framework with AES-GCM Encryption and Compress AI-Based Learned Compression

Abstract

The exponential growth of digital information exchange demands secure, efficient, and robust data encoding methods. This paper presents a unified data encoding framework integrating AES-GCM authenticated encryption with PBKDF2-based key derivation, Compress AI-driven learned compression, and a scalable multi- part encapsulation format with integrity verification via SHA-256 digests. The framework supports large payloads by dividing encrypted data into verifiable segments, enabling resilient storage and transmission. Optional image-quality enhancement using Real-ESRGAN or OpenCV EDSR is provided when the payload is visual media. Prototype evaluations show strong compression gains from Compress AI over classical JPEG+zlib baselines, strict integrity enforcement through AES-GCM tags, and accurate end-to-end reconstruction provided that all segments are available. The proposed approach is suited for privacy-preserving storage and controlled sharing in modern digital ecosystems.

Keywords: AES-GCM, PBKDF2, Compress AI, Learned Compression, Authenticated Encryption, Data Integrity, Multi-Part Encoding, Real-ESRGAN, EDSR, Secure Data Processing.