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Natural share-based lightweight (n, n) single secret image sharing scheme using LSB stuffing for medical images

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Abstract

Natural share-based secret image sharing (SIS) scheme is an important security mechanism used to secure medical images such as X-rays, MRIs, and CT scans. The most commonly used SIS method in state-of-the-art secret sharing schemes is based on random shares, which may raise suspicion for the attacker, even though they do not reveal any sensitive information. To mislead the attacker, it is necessary to generate shares that cannot be identified as secret shares. Existing works on natural shares are often complex, using operations such as Fractal Matrix, Turtle Shell, Hybrid Fractal Matrix, and others. In this research, a natural share-based lightweight (n, n) single SIS scheme using LSB stuffing for medical images is proposed. The suggested technique uses a lightweight operation called Boolean XOR and LSB stuffing for share generation and secret reconstruction, which are less sophisticated than the operations used in existing schemes. The secret image is used to generate n different natural shared images, and all n natural shared images are necessary to rebuild the original secret image. The suggested approach generates natural shares rather than random ones, which misleads the attacker into interpreting them as non-confidential information. This makes the natural shares less vulnerable to attack and helps keep them safe. Experimental findings show that the shares produced by the suggested method are visually appealing and do not disclose any confidential information. Even if the attacker knows that the natural shares contain confidential information, the secret cannot be reconstructed until the attacker has all the natural shares. Moreover, the secret won’t be reconstructed at the receiver end if any of the natural shares get corrupted. However, the suggested scheme has a limitation that it only provides lossless recovery for shares having a value \(n \ge 4\).

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. National Institute of Technology, Uttarakhand, Srinagar, India

    Arjun Singh Rawat, Maroti Deshmukh & Maheep Singh

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  1. Arjun Singh Rawat
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  2. Maroti Deshmukh
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  3. Maheep Singh
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Contributions

Mr. AR done data collection, design of methodology, implementation, and manuscript preparation. Dr. MD (Corresponding author) and Dr. MS provide suggestions for design of the proposed model and done manuscript checking, analysis of the methodology, and results verification.

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Correspondence to Maroti Deshmukh.

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This article contains chest X-ray image, MRI image, and ultrasound image of human are free to use for commercial and non-commercial purposes and no permission is needed, available at: https://unsplash.com/photos/tHS9j3HWT1s, https://en.wikipedia.org/wiki/File:MRI_Head_Brain_Normal.jpg,https://commons.wikimedia.org/wiki/File:CRL_Crown_rump_length_12_weeks_ecografia_Dr._Wolfgang_Moroder.jpg, other non medical images are also free to use for commercial and non-commercial purposes and no permission is needed.

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Rawat, A.S., Deshmukh, M. & Singh, M. Natural share-based lightweight (n, n) single secret image sharing scheme using LSB stuffing for medical images. J Supercomput 79, 19138–19167 (2023). https://doi.org/10.1007/s11227-023-05396-9

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