A cognitive approach for blockchain-based cryptographic curve hash signature (BC-CCHS) technique to secure healthcare data in Data Lake

被引:3
|
作者
Panwar, Arvind [1 ]
Bhatnagar, Vishal [2 ]
机构
[1] Guru Gobind Singh Indraprastha Univ, Univ Sch Informat Commun & Technol, Delhi, India
[2] Netaji Subhas Univ Technol, East Campus, Delhi, India
关键词
Data Lake; Electronic health record; Blockchain; Cryptographic curve hash signature; Hyperledger; Smart contract; Transaction; Cognitive approach; ACCESS-CONTROL; PRIVACY; RECORDS;
D O I
10.1007/s00500-021-06513-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In today's digital world, information is exchanged among various sources, and it is expected that each interaction or transaction among the sources must be reliable and secure. In these circumstances, blockchain technology can be applied to ensure healthcare data security in an efficient way. Blockchain is an ordered list of records linked together through a chain of blocks in a distributed ledger. It is a decentralized and tamper-proof database system. It can be used to store the medical records of patients and play a vital role in healthcare to maintain and share medical data securely. At present, many scholars are focusing on the privacy and security in electronic health record (EHR) sharing based on blockchain technology. But still, the security of health data plays a significant challenge. A Cognitive Approach blockchain-based cryptographic curve hash signature (BC-CCHS) technique is proposed to secure patients' medical records and share their personal health data safely and conveniently. The proposed approach is carried out in the Hyperledger framework. Here, several phases like registration, authentication, uploading, and requesting are involved in enhancing the security mechanism. The proposed methodology is experimentally tested and validated with the existing techniques regarding encryption time, decryption time, throughput, delay, and overall processing time.
引用
收藏
页码:429 / 429
页数:15
相关论文
共 25 条
  • [1] A Review of Blockchain-Based Secure Sharing of Healthcare Data
    Xi, Peng
    Zhang, Xinglong
    Wang, Lian
    Liu, Wenjuan
    Peng, Shaoliang
    APPLIED SCIENCES-BASEL, 2022, 12 (15):
  • [2] Blockchain-based cryptographic approach for privacy enabled data integrity model for IoT healthcare
    Chandol, Mohan Kumar
    Rao, M. Kameswara
    JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2023, 37 (01) : 53 - 74
  • [3] Data Provenance for healthcare: a blockchain-based approach
    D'Antonio, Salvatore
    Uccello, Federica
    2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022), 2022, : 1655 - 1660
  • [4] HealthBlock: A secure blockchain-based healthcare data management system
    Zaabar, Bessem
    Cheikhrouhou, Omar
    Jamil, Faisal
    Ammi, Meryem
    Abid, Mohamed
    Computer Networks, 2021, 200
  • [5] A Blockchain-Based Healthcare Platform for Secure Personalised Data Sharing
    Bowles, Juliana
    Webber, Thais
    Blackledge, Euan
    Vermeulen, Andreas
    PUBLIC HEALTH AND INFORMATICS, PROCEEDINGS OF MIE 2021, 2021, 281 : 208 - 212
  • [6] HealthBlock: A secure blockchain-based healthcare data management system
    Zaabar, Bessem
    Cheikhrouhou, Omar
    Jamil, Faisal
    Ammi, Meryem
    Abid, Mohamed
    COMPUTER NETWORKS, 2021, 200
  • [7] Secure transfer of robust healthcare data using blockchain-based privacy
    Kumar, Maddila Suresh
    Nagalakshmi, Vadlamani
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (02): : 1275 - 1291
  • [8] A Secure Blockchain-Based Solution for Management of Pandemic Data in Healthcare Systems
    Dalwani, Arav
    BLOCKCHAIN AND APPLICATIONS, 2022, 320 : 189 - 198
  • [9] Secure transfer of robust healthcare data using blockchain-based privacy
    Maddila Suresh Kumar
    Vadlamani Nagalakshmi
    Cluster Computing, 2024, 27 : 1275 - 1291
  • [10] A Blockchain-Based Scheme for Secure Data Offloading in Healthcare With Deep Reinforcement Learning
    He, Qiang
    Feng, Zheng
    Fang, Hui
    Wang, Xingwei
    Zhao, Liang
    Yao, Yudong
    Yu, Keping
    IEEE-ACM TRANSACTIONS ON NETWORKING, 2024, 32 (01) : 65 - 80