A hybrid encryption algorithm based approach for secure privacy protection of big data in hospitals

Aiming at the hidden danger of information security caused by the lack of medical big data information security firewall, this paper proposes a security privacy protection method for hospital big data based on hybrid encryption algorithm. First, collect hospital big data including hospital medical business system, mobile wearable devices and big health data; Secondly, use byte changes to compress hospital big data to achieve safe transmission of hospital big data; Then, the hospital sender uses the AES session key to encrypt the hospital big data and the ECC public key to encrypt the AES session key, uses SHA-1 to calculate the hash value of the medical big data, and uses the ECC public key to sign the hash value; The hospital receiver uses the ECC private key to verify the signature, and decrypts the AES session key using the ECC private key. After the AES session key decrypts, the hospital big data, the hospital big data security privacy protection is completed. The experimental results show that the method is superior to conventional ECC algorithm or RSA and AES hybrid encryption algorithm in terms of encryption and decryption time and security strength. The average correlation coefficient of encrypted hospital big data is only 0.0576, and the RL curve value is low and gentle. The encrypted data has good scrambling effect and low privacy leakage probability, which ensures the confidentiality and integrity of medical data in the transmission process.

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Publication Details

Journal
Egyptian Informatics Journal
Published
2024-11-05
DOI
https://doi.org/10.1016/j.eij.2024.100569
Citations
2
Primary Topic
Cloud Data Security Solutions
Type
article
Field-Weighted Citation Impact
1.56
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article

A hybrid encryption algorithm based approach for secure privacy protection of big data in hospitals

Qian Huang, Wei Li
2 citations
Egyptian Informatics Journal
Cloud Data Security Solutions
1.56
article

A hybrid encryption algorithm based approach for secure privacy protection of big data in hospitals

Qian Huang, Wei Li
article en
2 citations

Abstract

Aiming at the hidden danger of information security caused by the lack of medical big data information security firewall, this paper proposes a security privacy protection method for hospital big data based on hybrid encryption algorithm. First, collect hospital big data including hospital medical business system, mobile wearable devices and big health data; Secondly, use byte changes to compress hospital big data to achieve safe transmission of hospital big data; Then, the hospital sender uses the AES session key to encrypt the hospital big data and the ECC public key to encrypt the AES session key, uses SHA-1 to calculate the hash value of the medical big data, and uses the ECC public key to sign the hash value; The hospital receiver uses the ECC private key to verify the signature, and decrypts the AES session key using the ECC private key. After the AES session key decrypts, the hospital big data, the hospital big data security privacy protection is completed. The experimental results show that the method is superior to conventional ECC algorithm or RSA and AES hybrid encryption algorithm in terms of encryption and decryption time and security strength. The average correlation coefficient of encrypted hospital big data is only 0.0576, and the RL curve value is low and gentle. The encrypted data has good scrambling effect and low privacy leakage probability, which ensures the confidentiality and integrity of medical data in the transmission process.

Egyptian Informatics JournalVol. 28
Wuhan Polytechnic University (CN), Wuhan University (CN), Wuchang University of Technology (CN), Renmin Hospital of Wuhan University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 12%
Cloud Data Security Solutions
1.56
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A hybrid encryption algorithm based approach for secure privacy protection of big data in hospitals — Qian Huang, Wei Li · Egyptian Informatics Journal (2024) | TGRS Research Map | TGRS