| dc.description.abstract |
The low-cost, accessing flexibility, agility, and mobility of cloud infrastructures have attracted medical organizations
to store their high-resolution data in encrypted form. Besides storage, these infrastructures provide
various image processing services for plain (non-encrypted) images. Meanwhile, the privacy and security
of uploaded data depend upon the reliability of the service provider(s). The enforcement of laws towards
privacy policies in health-care organizations, for not disclosing their patient’s sensitive and private medical
information, restrict them to utilize these services. To address these privacy concerns for melanoma detection,
we propose CryptoLesion, a privacy-preserving model for segmenting lesion region using whale optimization
algorithm (WOA) over the cloud in the encrypted domain (ED). The user’s image is encrypted using a permutation
ordered binary number system and a random stumblematrix. The task of segmentation is accomplished
by dividing an encrypted image into a pre-defined number of clusters whose optimal centroids are obtained
by WOA in ED, followed by the assignment of each pixel of an encrypted image to the unique centroid. The
qualitative and quantitative analysis of CryptoLesion is evaluated over publicly available datasets provided in
The International Skin Imaging Collaboration Challenges in 2016, 2017, 2018, and PH2 dataset. The segmented
results obtained by CryptoLesion are found to be comparable with the winners of respective challenges. CryptoLesion
is proved to be secure from a probabilistic viewpoint and various cryptographic attacks. To the best
of our knowledge, CryptoLesion is first moving towards the direction of lesion segmentation in ED. |
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