On modular inverse matrices a computation approach

Jacques-García, Fausto Abraham; Uribe-Mejía, Daniel; Macías-Bobadilla, Gonzalo; Sánchez, Ricardo Chaparro

doi:10.46932/sfjdv3n3-005

Cited by 1 publication

(1 citation statement)

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“…In ( 14) and ( 15), . The novelty of generating mmi"s by the Householder technique is that mmi"s are obtained without directly calculating the matrix inverses in Zp (with time complexity [39]), but using only scalar modular inverse as in ( 5) and (7), which has a time complexity of only [40].…”

Section: ) Modified Householder Construction: Conventionalmentioning

confidence: 99%

Reversible De-identification of Specific Regions in Biomedical Images and Secured Storage by Randomized Joint Encryption

Prabhavathi¹,

B²

2023

IJACSA

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In many circumstances, de-identification of a specific region of a biomedical image is necessary. Deidentification is used to hide the subject's identity or to prevent the display of the objectionable or offensive region(s) of the image. The concerned region can be blurred (de-identified) by using a suitable image processing technique guided by the regiondefining mask. The proposed method provides lossless blurring, which means the original image can be recovered fully with zero loss. The blurred image and the region-defining mask, along with the digital signature, are jointly encrypted to form the composite cipher matrix, and it is stored in the cloud for further distribution. The composite cipher matrix is decrypted to recover the blurred image by the conventional end user. Further, using the deblur key, the original image can be recovered with zero loss by the fully authorized special end users. On decryption, the digital signature is available for both types of end users. The proposed method uses randomized joint encryption using integer matrix keys in a finite field. The experimental results show that the proposed method achieves a reduction in the average execution time of encryption by 30 to 40 percent compared to its nearest competitor. Additionally, the proposed scheme achieves very nearly ideal performance with reference to the correlation coefficient, entropy, pixel change rate, and structural similarity index. Overall, the proposed algorithm performs substantially better than the other similar existing schemes for large-sized images.

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Section: ) Modified Householder Construction: Conventionalmentioning

confidence: 99%