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CryptoLesion: A Privacy-preserving Model for Lesion Segmentation Using Whale Optimization over Cloud

Published: 11 June 2020 Publication History

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 stumble matrix. 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.

Supplementary Material

a50-tanwar-suppl.pdf (tanwar.zip)
Supplemental movie, appendix, image and software files for, CryptoLesion: A Privacy-preserving Model for Lesion Segmentation Using Whale Optimization over Cloud

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 2
May 2020
390 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3401894
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 June 2020
Online AM: 07 May 2020
Accepted: 01 January 2020
Revised: 01 January 2020
Received: 01 May 2019
Published in TOMM Volume 16, Issue 2

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Author Tags

  1. Privacy preserving
  2. cloud computing
  3. melanoma detection
  4. permutation ordered binary number system
  5. whale optimization

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  • Refereed

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  • University Grants Commission, INDIA

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