The “Intelligent privacy‐preserving data management framework for the medicine supply chain system,” reported by SECURITY AND PRIVACY, addresses the integration of blockchain technology with artificial intelligence (AI) to improve the security and efficiency of pharmaceutical data management. Traditional blockchain methods face limitations in scalability and data validation, which the proposed framework aims to overcome. By combining machine learning (ML) algorithms with blockchain, the framework enhances the accuracy and security of medicine supply chain data. This hybrid approach ensures that only valid data is stored on the blockchain, addressing issues related to fake data and data provenance, which have been persistent concerns in existing systems.
Framework Structure
The framework utilizes ML to classify valid and invalid data within the medicine supply chain, ensuring that only verified data is stored on the blockchain. This integration allows the blockchain to perform pre-storage validation, mitigating the risks associated with fake data entries. To further optimize the process, the framework employs an InterPlanetary File System (IPFS) to store medicine supply chain data, computing its hash and storing it on a private Hyperledger Fabric blockchain. This decentralized storage approach reduces the need for extensive storage space, enhancing the scalability of the blockchain system.
The proposed system’s performance was evaluated in two distinct phases: the machine learning phase and the blockchain phase. During the ML phase, the model’s performance was measured using statistical methods, ensuring its effectiveness in data classification. In the blockchain phase, various performance parameters, such as throughput (618 transactions per second), latency (0.12 seconds), response time (11 seconds), and data rate (282 Mbps), were assessed to determine the efficiency of the framework. These evaluations indicate a significant improvement in the management and security of the medicine supply chain.
Comparative Insights
Comparing the proposed framework to previous methodologies reveals notable advancements. Earlier blockchain systems often struggled with scalability and storage constraints, as they required the storage of entire files, consuming substantial space and reducing efficiency. The integration of IPFS and Hyperledger Fabric in the new framework addresses these issues by enabling decentralized storage and minimizing storage requirements. This approach not only optimizes data storage but also improves retrieval times, which are critical for the fast-paced pharmaceutical industry.
Additionally, previous attempts at integrating blockchain with supply chain management lacked robust data validation mechanisms, resulting in the potential for fake data entries. The current framework’s use of ML algorithms for data validation before storage marks a significant improvement, ensuring higher data integrity and reliability. These enhancements are crucial for maintaining the trustworthiness of the medicine supply chain, which is essential for patient safety and regulatory compliance.
The integration of AI and blockchain in the proposed framework offers a comprehensive solution to longstanding issues in the pharmaceutical supply chain. By combining ML-based data validation with decentralized storage via IPFS and Hyperledger Fabric, the framework enhances both the security and scalability of the system. The performance evaluations highlight the framework’s efficiency, demonstrating its potential to revolutionize data management in the pharmaceutical industry. This holistic approach not only mitigates the risk of fake data but also ensures efficient data handling, which is vital for the industry’s operational success.
