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

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.

@article{ijter2025212509107025,
  author = {Kurella Padma and Dr G.Sharmila Sujatha},
  title = {A Secure Data Encoding Framework with AES-GCM Encryption and Compress AI-Based  Learned Compression},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2025},
  volume = {1},
  number = {5},
  pages = {23-32},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212509107025/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.},
  month = {Sep},
}

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