基于InterPlanetary文件系统和改进bloom树的区块链医疗记录高效存储和检索设计

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Security and Privacy Pub Date : 2023-02-05 DOI:10.1002/spy2.301
S. Sathiya Devi, Arumugam Bhuvaneswari
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引用次数: 0

摘要

在医疗保健部门,医疗记录包含患者的敏感信息,因此保证其机密性和完整性至关重要。为了提高它的安全性,区块链技术正在被利用。区块链是一种分布式账本,它安全地保存数据,同时在不需要第三方的情况下产生信任。它具有数据存储约束,Merkle树保持了数据的完整性,但在其中搜索事务时效率低下。因此,本文描述了基于行星间文件系统(IPFS)的存储和改进的bloom树数据结构,该结构是bloom filter和Merkle树形的混合,用于高效搜索。为了保护数据隐私,最初它使用基于密文策略属性的加密对医疗记录进行加密,然后存储在IPFS上的数据返回哈希值。为了降低误报率(FPR),IPFS返回的哈希被存储在布隆过滤器的两个部分中。第一部分使用“k”非加密哈希函数存储数据,第二部分使用相同的哈希函数存储转换后的数据。bloom树是使用Merkle证明创建的,用于验证区块链中的医疗记录。实验表明,该方法降低了FPR率,搜索复杂度为O(log2)。
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Design of efficient storage and retrieval of medical records in blockchain based on InterPlanetary File System and modified bloom tree
In the healthcare sector, medical records contain sensitive information about patients, so guaranteeing the confidentiality and integrity of it is essential. To improve the security of it, blockchain technology is being utilized. The blockchain is a type of distributed ledger and it keeps data securely while also generating trust without the need of third party. It has data storage constraint and Merkle tree preserves data integrity but it is inefficient when searching transactions within it. Hence this paper describes InterPlanetary File System (IPFS) based storage and modified bloom tree data structure which is a hybridization of bloom filter and Merkle tree for efficient searching. To protect data privacy, initially it encrypts medical records using ciphertext policy‐attribute based encryption and then the data stored on IPFS returns a hash value. To diminish the false positive rate (FPR), the hash returned by IPFS is stored in two parts of the bloom filter. The first part stores the data by using “k” non‐cryptographic hash function and second part stores the transformed data with the same hash function. The bloom tree is created using Merkle proof for verification of medical record in blockchain. The experiments show that the proposed method reduces the FPR rate and searching complexity is O(log2).
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