Blockchain-Enabled Decentralized Data Lakes for Secure and Transparent AI-Ready Data Storage

Due to the exponential growth of AI driven applications, it is absolutely necessary that the data storage needs to be secure, transparent and scalable. Traditional centralized data lakes are lack transparency, do not provide data breach, high operational cost. The contributions of this paper include proposing a Blockchain Enabled Decentralized Data Lake (DAIDL), which is a new framework for AI capable data storage of secure, private, and easy access to data. While the proposed architecture involves blockchain for metadata management, decentralized storage (IPFS, Filecoin, Arweave) for data distribution as well as the federated learning for AI model training without exposing raw data. Besides, privacy preserving AI analytics with homomorphic encryption and secure multi-party computation (SMPC). It provides data integrity with a smart contract driven access control mechanisms and an incentivization for the contributors in a tokenized data economy. We show the implementation scales to be high, and is more secure and less expensive than traditional cloud based data lakes. Results of the experiments plus show that while storage efficient, retrieval speed of information is improved and the system is also less susceptible to unauthorized access. DAIDL provides freedom of choice to handle AI applications in critical areas like healthcare, financial sector, and smart cities in a way that data sovereignty and compliance with regulations are satisfied. Building on this, this research offers a roadmap for dealing with the limitations of scalability, interoperability and quantum secure scheme for decentralized AI ready storage system.