Publications
DNAcrypt-AI protocol for generating and encrypting a secret key
Abstract: DNAcrypt-AI is a human genome-inspired cryptographic framework that leverages k-mer-based alphanumeric encoding, genome vocabulary, sequence reconstruction, and sequence-informed machine learning to generate and encrypt digital credentials as genome coordinates. This protocol describes the procedure for generating a fixed-length secret key (16 characters) and encrypting it using DNAcrypt-AI. Unlike passwords that may comprise alphanumeric characters and symbols, secret keys are devoid of symbols. The pipeline is implemented on Google Colab and integrates a high-throughput DNA sequence reconstitution pipeline (FAS2rDNA) with a sequence intelligence model (Covary) to randomly generate a plaintext key into an encrypted, non-human-readable genome metadata representation. The protocol generated the expected secret key of 16 characters long and provided an encryption data that can be used for decryption and password recovery.
DNAcrypt-AI protocol for generating and encrypting a password
Abstract: DNAcrypt-AI is a human genome-inspired cryptographic framework that leverages k-mer-based alphanumeric and symbol encoding, genome vocabulary, sequence reconstruction, and sequence-informed machine learning to generate and encrypt digital credentials as genome coordinates. This protocol describes the procedure for generating a fixed-length password (16 characters) and encrypting it using DNAcrypt-AI. The pipeline is implemented on Google Colab and integrates a high-throughput DNA sequence reconstitution pipeline (FAS2rDNA) with a sequence intelligence model (Covary) to randomly generate a plaintext password into an encrypted, non-human-readable genome metadata representation. The protocol generated the expected password text of 16 characters long and provided an encryption data that can be used for decryption and password recovery.
Decryption of genome-encoded cryptographic keys using DNAcrypt-AI
Abstract: DNAcrypt-AI is a human genome-based cryptographic framework that leverages k-mer-based alphanumeric encoding, genome vocabulary, sequence reconstruction, and sequence-informed machine learning to enable controlled decryption of encrypted digital credentials. This protocol describes the procedure for decrypting an encrypted secret key generated using DNAcrypt-AI, restoring the original fixed-length plaintext key from its genome metadata representation. The decryption workflow is implemented on Google Colab and integrates a high-throughput DNA sequence reconstitution pipeline (FAS2rDNA) with a sequence intelligence model (Covary) to reconstruct the encrypted genome coordinates into a human-readable secret key. The protocol successfully recovered the expected 16-character secret key, demonstrating reproducible reversibility of the DNAcrypt-AI encryption-decryption cycle for research and educational use.






