z-logo
open-access-imgOpen Access
Partial image encryption using format-preserving encryption in image processing systems for Internet of things environment
Author(s) -
Jang Wonyoung,
Lee Sun-Young
Publication year - 2020
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1177/1550147720914779
Subject(s) - computer science , encryption , image processing , pixel , masking (illustration) , histogram , nist , computer security , entropy (arrow of time) , computer vision , image (mathematics) , artificial intelligence , speech recognition , art , visual arts , physics , quantum mechanics
Concomitant with advances in technology, the number of systems and devices that utilize image data has increased. Nowadays, image processing devices incorporated into systems, such as the Internet of things, drones, and closed-circuit television, can collect images of people and automatically share them with networks. Consequently, the threat of invasion of privacy by image leakage has increased exponentially. However, traditional image-security methods, such as privacy masking and image encryption, have several disadvantages, including storage space wastage associated with data padding, inability to decode, inability to recognize images without decoding, and exposure of private information after decryption. This article proposes a method for partially encrypting private information in images using FF1 and FF3-1. The proposed method encrypts private information without increasing the data size, solving the problem of wasted storage space. Furthermore, using the proposed method, specific sections of encrypted images can be decrypted and recognized before decryption of the entire information, which addresses the problems besetting traditional privacy masking and image encryption methods. The results of histogram analysis, correlation analysis, number of pixels change rate, unified average change intensity, information entropy analysis, and NIST SP 800-22 verify the security and overall efficacy of the proposed method.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom