Blockchain-Integrated Deep Learning for Secure Health Data Sharing and Consent Management

Abstract

The collection of medical data encompasses a variety of patient records that hold significant value for ongoing treatment and future research works. To ensure the privacy of the data, it is imperative to store and share it securely. Utilizing blockchain technology in managing healthcare data is prevalent due to its decentralized nature and ability to provide tamper-proof security measures. In healthcare's dynamic and ever-changing realm, the importance of securely sharing sensitive health data and managing consent effectively cannot be overstated. These factors are crucial in safeguarding patient privacy and promoting collaborative research efforts. This study aims to investigate the potential integration of blockchain technology and Long Short-Term Memory (LSTM) deep learning models to develop a comprehensive framework that ensures secure health data sharing and effective consent management. Integrating blockchain technology's decentralized and immutable ledger with the sequential learning capabilities of LSTM presents a promising approach to tackle the various obstacles related to data integrity, provenance, and patient-centric consent within healthcare ecosystems. The proposed system aims to improve the security and transparency of health data transactions while enabling dynamic consent management. This empowers individuals to have more control over their data. In this study, aims to assess the effectiveness of the blockchain-integrated LSTM model in ensuring health data security. Additionally, the investigated model facilitates smooth and privacy-preserving collaboration among various healthcare stakeholders. The experiments involved utilizing two publicly available data sources, CICIDS-2017 and NSL-KDD. These experiments evaluated the proposed model's performance compared to existing state-of-the-art approaches within non-blockchain and blockchain settings. The results demonstrated that the proposed model exhibited superior performance across both datasets, achieving an accuracy rate of approximately 99%.

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