Attack resistant blockchain-based healthcare record system using modified RSA Algorithm

Srivastava, Arushi; Gupta, Juhi

doi:10.1007/s41870-023-01588-x

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Sribastava and Gupta [35] worked on preserving the integrity of electronic health record, which constituted a similar concept to document forgery detection. The authors introduced a method that allowed each patient's information to be recorded in a blockchain-based system.…”

Section: Existing Research and Technologiesmentioning

confidence: 99%

“…This approach applied the same idea as our proposed system in utilizing the capability of hash functions, but our method was different because blockchain was not required to achieve the objective of checking data integrity. Another research closely related to [24,35] was proposed by Gowda and Malakreddy [10], who designed a blockchain-based privacy preservation mechanism for fog computing environment. However, this mechanism only focused on the privacy of data, rather than forgery detection or the immutability side of the data.…”

Section: Existing Research and Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

Design of an academic document forgery detection system

Boonkrong

2024

Int. j. inf. tecnol.

View full text Add to dashboard Cite

Advances in digital technology and communication are reducing the popularity of physical or paper-based documents, particularly in the official academic field. Many academic institutions have shifted from paper-based academic documents to electronic formats. However, with the availability of document-editing and photo-editing software, an adversary can illegally modify original academic documents to suit their needs. Therefore, it is essential that receivers of these digital academic documents must verify the authenticity and detect the forgery of the documents. In this study, a cryptographic hash function, which is concerned with data integrity, is used to detect modifications of digital academic documents. Experiments were conducted to evaluate the correctness and speed of academic document verification. The proposed system was compared with other conventional methods such as digital signature-based, convolutional neural network, and blockchain-based methods. Our study shows that the proposed system can efficiently detect forged digital academic documents with 100% accuracy and a verification speed of 0.352 ms, which is considerably faster than the current digital signature-based, convolutional neural network and blockchain-based technologies. The proposed cryptographic hash function-based method provides another way to help detect forged academic documents.

show abstract

Section: Existing Research and Technologiesmentioning

confidence: 99%