Blockchain-Enabled Privacy Mechanisms for Distributed AI Systems

The growing adoption of distributed artificial intelligence (AI) systems has introduced new challenges in data security, trust, and privacy. Traditional centralized architectures are often vulnerable to data breaches and lack transparency, making them unsuitable for sensitive applications. Blockchain technology, with its decentralized and tamper-resistant design, offers promising solutions to these concerns. By integrating blockchain with distributed AI, organizations can ensure secure data sharing, traceable model updates, and enhanced privacy-preserving mechanisms. This study explores the intersection of blockchain and privacy in distributed AI systems, examining cryptographic methods, consensus protocols, and smart contract frameworks that strengthen data confidentiality. Furthermore, it highlights real-world use cases where blockchain enhances trust among participants without compromising performance or scalability. The findings emphasize that blockchain-enabled privacy mechanisms are not only technically viable but also essential for building resilient, transparent, and ethically responsible AI ecosystems.