A Study on Cryptographic Algorithms for Data Confidentiality and Integrity
Keywords:
AI-driven fraud detection, Blockchain technology, Covert data hiding, Cryptography, Data integrity, Decentralized framework, Digital communication, Distributed Ledger Technology (DLT), Encryption and decryption, End-to-end security, Least Significant Bit (LSB), Multimodal steganography, Proof of Stake (PoS), Proof of Work (PoW), Quantum-resistant cryptography, Secure data transactions, Secure multimedia transactions, SHA-256 hashing, Steganography, Tamper resistanceAbstract
In today’s digital landscape, safeguarding sensitive information during transactions has become increasingly critical. This project focuses on developing a secure data exchange framework by integrating cryptographic techniques, blockchain technology, and steganography. Inspired by blockchain-based asset management systems and decentralized transaction models, the study employs cryptographic methods like SHA-256 hashing, Proof of Work (PoW), and Proof of Stake (PoS) to ensure data integrity, authenticity, and confidentiality.
To further enhance security, the project incorporates multimodal steganography and Least Significant Bit (LSB) techniques for embedding sensitive information within digital media. The combined strengths of blockchain’s immutable ledger and the covert nature of steganography create a system that prioritizes end-to-end security, privacy, and resistance to tampering. The methodology involves creating a decentralized peer-to-peer network for validating transactions, using advanced encryption techniques, and applying steganographic methods to conceal data within multimedia formats.
Testing demonstrates the system’s ability to prevent data breaches, support secure transactions, and withstand cyberattacks. This innovative hybrid model offers a reliable solution for secured digital communications and transactions across finance, healthcare, and e-commerce domains. Future advancements could explore quantum-resistant cryptographic approaches and AI-driven mechanisms to detect and mitigate fraud.
