4.5 Article

A robust and secure image sharing scheme with personal identity information embedded

Journal

COMPUTERS & SECURITY
Volume 85, Issue -, Pages 107-121

Publisher

ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.cose.2019.04.010

Keywords

Image security; Secret image sharing; Compressive sensing; Robustness; Authentication

Funding

  1. Guangxi Key Laboratory of Trusted Software [kx201904]
  2. Fundamental Research Funds for the Central Universities [XDJK201913010]
  3. Natural Science Foundation of China [61773320, 61602158]
  4. Natural Science Foundation Project of Chongqing CSTC [cstc2018jcyjAX0583]
  5. Research Foundation of the Education Department of Jiangxi Province [G11170322]
  6. Science and Technology Research Project of Henan Province [182102210374]

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In a (t, n)-threshold secret image sharing (SIS) scheme, the secrecy of a secret image relies on check and balance among participants. Unfortunately, the decentralized management also brings an underlying security flaw, i.e., once an attacker impersonates an authorized participant to submit a fake shadow for reconstruction no information about the secret image can be revealed. In order to resist such an identity-based attack, a novel SIS scheme with identity-based authentication is proposed, where a natural image is encoded through compressive sensing and then the compressive image is divided into n shadows (i.e., shares) according to an innovate way in which the production of shadows is controlled by participants' identification number. Only when any t or more authorized participants cooperate together can the original image be reconstructed. Besides, a simple but efficient non-uniform scalar quantization method and a group communication protocol are presented to perfect the proposed scheme. Performance and security analyses indicate that our scheme possesses many advantages, including no pixel expansion, flexible shadow size, progressive display capability, error-resilient capability, no necessity for secure channels, and high security against statistical attack, brute-force attack, and identity-based attack. (C) 2019 Elsevier Ltd. All rights reserved.

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