Integrating Deep Learning with Blockchain Technology to Strengthen Decentralized AI Application's Data Privacy, Security, and Transparency

Deploying new systems of Artificial Intelligence (AI), especially deep learning, has led to revolutionary use cases in every industry. But these innovations also come with greater concerns about data privacy, data security, and transparency - especially when it comes to sensitive user data. And one of the most innovative technologies that is there to help tackle these challenges is blockchain technology given its decentralized, immutable and transparent nature. Inspired by the mechanism behind deep learning and blockchain, the research successfully investigates the possibility of combining deep learning u blockchain together to realise the decentralised AI applications while ensuring data integrity, privacy, и security. In this paper, we present a new framework that merges the efficiency of deep learning models with the decentralized structure of blockchain. The access and data sharing security enforced by smart contracts and federated learning of deep learning model takes place in the protected way without disclosure of raw data. By logging model updates, transactions and decisions - blockchain allows data immutability and auditability - increasing trust among the stakeholders. A case study of a privacy-sensitive application, e.g., healthcare or financial services, is designed, which helps validate the proposed approach. The experimental results show that the integrated system realizes robust model performance and guarantees data privacy and transparent operation. Moreover, we analyze the scalability of the solution in terms of the latency and throughput of blockchain transactions and the efficiency of federated learning at distributed nodes. The study offers important insights for surmounting challenge related to merging deep learning and blockchain e.g., computational overhead, scalability and energy efficiency. Additionally, we discuss how such an integrated paradigm could foster the development of a new kind of decentralized AI applications, leading to ethical and secure AI systems that comply with existing global data privacy laws such as GDPR and CCPA. The implications of this research are significant, as they highlight the potential of blockchainintegrated deep learning systems to promote secure, transparent, and privacy-preserving decentralized AI applications, thereby laying the groundwork for future advancements in secure AI deployment.