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Paranoia File & Text Encryption, published by Paranoia Works and currently available in version 17.2.43 with five recorded versions overall, is a security and privacy application designed to encrypt files, folders, and text using strong cryptographic algorithms. The software falls within the encryption and data protection category and is suitable for safeguarding private and confidential information from unauthorized access. The application comprises two distinct modules. The File Encryptor module allows users to securely encrypt individual files or entire folders simply by dragging them into the application window, functioning similarly to an archiver by producing a new .enc file once encryption is complete. The Text Encryptor module is intended for protecting messages, notes, cryptocurrency keys such as seeds and mnemonics, and other text-based information. Beyond conventional encryption, the software incorporates steganographic capabilities based on the F5 and J-UNIWARD algorithms, enabling users to conceal sensitive data, and it supports post-quantum secure password exchange through ML-KEM, the key encapsulation mechanism standardized in NIST FIPS 203. This combination of features makes the tool relevant for use cases ranging from everyday document protection to securing cryptocurrency credentials and exchanging passwords in a manner resistant to future quantum computing attacks. The application supports a broad selection of encryption algorithms, giving users flexibility in balancing compatibility and security requirements. Available algorithms include Threefish at 1024-bit, SHACAL-2 at 512-bit, the proprietary Paranoia C4 at 2048-bit, AES at 256-bit, RC6 at 256-bit, Serpent at 256-bit, Blowfish at 448-bit, and Twofish at 256-bit. With its dual-module design, drag-and-drop workflow, steganography support, and integration of a NIST-standardized post-quantum key exchange, Paranoia File & Text Encryption serves individuals and professionals seeking comprehensive, modern cryptographic protection for both files and textual data.
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