Genetic Privacy Shields: A DNA Steganography Approach for Multi-Level Text Encryption : Unveiling the Future of Genetic Data Protection

In the current era genetic sequencing has emerged as an indispensable methodology for examining diverse DNA profiles across domains encompassing healthcare, agriculture, and forensic science. To enhance precision, a range of techniques, including high-throughput shotgun sequencing, have evolved over time. Recent strides in next-generation sequencing methodologies have facilitated the embedding of data within DNA using synthesized oligonucleotides. Notably, significant efforts have been dedicated to embedding extensive volumes of information within living organisms to safeguard intellectual property. When it comes to DNA steganography, traditional detection methods, exemplified by frequency analysis-based approaches, often overlook crucial signals and are susceptible to newly emerging steganography techniques. This study undertakes a comprehensive analysis of inherent allocations, distribution variance computation, and the classification of sequences as coding or non-coding. Our research endeavors to devise an advanced security strategy employing DNA steganography. Central to our proposition is the utilization of DNA steganography to forge a robust security framework. In this study, we introduce the DNA-Genetic Encryption (D-GET) mechanism to augment security. The D-GET technique involves the binarization of digital data followed by its conversion into DNA sequences. Subsequently, a sequence of operations, including reshaping, encryption, crossover, and mutation, is performed iteratively to heighten the robustness and unpredictability of the technique. The core processes of D-GET are iterated at least three times. Encrypted data is transmitted in either text or image file formats. Upon reception, the D-GET approach is employed to decode and restore the acquired data to its original form. A distinguishing aspect of our approach is the transformation of textual content into visual representations and vice versa, augmenting security measures. Multiple key sequences are leveraged to amplify the degree of dispersion and ambiguity, rendering the final cipher data highly intricate to decipher. Empirical observations highlight that our proposed methodology affords multi-layered security attributes, fortified by multi-stage and genetic operations, rendering it resilient against diverse threats and affording an elevated level of safeguarding. Notably, the methodology's efficacy derives from the pronounced divergence between the transformed information and the confidential content, thereby reinforcing the integrity of the encryption framework.