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Mathematics
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Mathematics, Cryptography, Secret Sharing, Information Hiding and Their Applications
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Mathematics, Cryptography, Secret Sharing, Information Hiding and Their Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: closed (10 January 2025) | Viewed by 10460

Special Issue Editors

Dr. Peng Li

E-Mail
Guest Editor
Department of Mathematics and Physics, North China Electric Power University, Baoding 071003, China
Interests: secret sharing; secret image sharing; polynomial-based secret image sharing; information hiding; multimedia security; information theory
Special Issues, Collections and Topics in MDPI journals
Dr. Xuehu Yan

E-Mail Website
Guest Editor
The College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
Interests: secret image sharing; information hiding; AI security; multimedia security; air-gapped
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Secret sharing and information hiding are key techniques to protect secret information. Secret sharing and information hiding are applied to access control, copyright protection, and blockchain distributive storage. AI is widely developed and used nowadays. However, AI security has attracted attention from researchers and engineers. In this Special Issue, we intend to apply secret sharing and information hiding to AI security.

Dr. Peng Li
Dr. Xuehu Yan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical model of secret sharing and information hiding
  • applications of secret sharing and information hiding
  • applying secret sharing and information hiding to enhance ai security
  • applying secret sharing and information hiding to deep learning
  • applying secret sharing and information hiding to authentication and copyright protection
  • applying secret sharing and information hiding to cloud computing and multi-party secure computing
  • applying secret sharing and information hiding to traffic protection
  • applying mathematics to information security and cyberspace situation awareness

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Published Papers (4 papers)

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Research

22 pages, 7582 KiB  
Article
Fast Frequency Domain Screen-Shooting Watermarking Algorithm Based on ORB Feature Points
by Yu Bai, Li Li, Shanqing Zhang, Jianfeng Lu and Mahmoud Emam
Mathematics 2023, 11(7), 1730; https://doi.org/10.3390/math11071730 - 4 Apr 2023
Cited by 7 | Viewed by 2403
Abstract
With high performances of image capturing tools, image information can be easily obtained by screenshots that make image copyright protection a challenging task. The existing screen-shooting watermarking algorithms suffer from a huge running time, in addition to their low robustness against different screenshot [...] Read more.
With high performances of image capturing tools, image information can be easily obtained by screenshots that make image copyright protection a challenging task. The existing screen-shooting watermarking algorithms suffer from a huge running time, in addition to their low robustness against different screenshot attacks, such as different distances and capturing angles of the screenshots. In this paper, a fast and robust high-capacity flexible watermarking algorithm for screenshot images is proposed. Firstly, Oriented FAST and Rotated BRIEF (ORB) feature points are extracted from the input image. Secondly, the feature points are then sorted in a descending order according to their response values. Then, the first five non-overlapping feature points are selected for the embedding by using Hamming window-based filtering method. Furthermore, we exploit the multi-resolution property of Discrete Wavelet Transform (DWT) and energy compaction property of Singular Value Decomposition (SVD) to embed the watermark. Therefore, the classical DWT combined with Singular Value Decomposition (SVD) are adopted to improve the robustness and capacity of the proposed watermarking algorithm. At the extraction side, the sum of the response values for the three RGB channels of the color-ripped image is calculated to improve the feature point localization accuracy. Experimental results show that the proposed screen-shooting watermarking algorithm improves running speed while ensuring the robustness. Furthermore, it has less time complexity and high robustness compared with the state-of-the-art watermarking algorithms against different screenshot attacks. Full article
(This article belongs to the Special Issue Mathematics, Cryptography, Secret Sharing, Information Hiding and Their Applications)
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18 pages, 997 KiB  
Article
A Study of Privacy-Preserving Neural Network Prediction Based on Replicated Secret Sharing
by Yanru Zhang and Peng Li
Mathematics 2023, 11(4), 1048; https://doi.org/10.3390/math11041048 - 19 Feb 2023
Viewed by 1786
Abstract
Neural networks have a wide range of promise for image prediction, but in the current setting of neural networks as a service, the data privacy of the parties involved in prediction raises concerns. In this paper, we design and implement a privacy-preserving neural [...] Read more.
Neural networks have a wide range of promise for image prediction, but in the current setting of neural networks as a service, the data privacy of the parties involved in prediction raises concerns. In this paper, we design and implement a privacy-preserving neural network prediction model in the three-party secure computation framework over secret sharing of private data. Secret sharing allows the original data to be split, with each share held by a different party. The parties cannot know the shares owned by the remaining collaborators, and thus the original data can be kept secure. The three parties refer to the client, the service provider and the third server that assist in the computation, which is different from the previous work. Thus, under the definition of semi-honest and malicious security, we design new computation protocols for the building blocks of the neural network based on replicated secret sharing. Experimenting with MNIST dataset on different neural network architectures, our scheme improves 1.3×/1.5× and 7.4×/47.6× in terms of computation time as well as communication cost compared to the Falcon framework under the semi-honest/malicious security, respectively. Full article
(This article belongs to the Special Issue Mathematics, Cryptography, Secret Sharing, Information Hiding and Their Applications)
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26 pages, 9664 KiB  
Article
Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques
by Ahmed Sedik, Ahmed A. Abd El-Latif, Mudasir Ahmad Wani, Fathi E. Abd El-Samie, Nariman Abdel-Salam Bauomy and Fatma G. Hashad
Mathematics 2023, 11(3), 703; https://doi.org/10.3390/math11030703 - 30 Jan 2023
Cited by 11 | Viewed by 2692
Abstract
Cybersecurity has been one of the interesting research fields that attract researchers to investigate new approaches. One of the recent research trends in this field is cancelable biometric template generation, which depends on the storage of a cipher (cancelable) template instead of the [...] Read more.
Cybersecurity has been one of the interesting research fields that attract researchers to investigate new approaches. One of the recent research trends in this field is cancelable biometric template generation, which depends on the storage of a cipher (cancelable) template instead of the original biometric template. This trend ensures the confidential and secure storage of the biometrics of a certain individual. This paper presents a cancelable multi-biometric system based on deep fusion and wavelet transformations. The deep fusion part is based on convolution (Conv.), convolution transpose (Conv.Trans.), and additional layers. In addition, the deployed wavelet transformations are based on both integer wavelet transforms (IWT) and discrete wavelet transforms (DWT). Moreover, a random kernel generation subsystem is proposed in this work. The proposed kernel generation method is based on chaotic map modalities, including the Baker map and modified logistic map. The proposed system is implemented on four biometric images, namely fingerprint, iris, face, and palm images. Furthermore, it is validated by comparison with other works in the literature. The comparison reveals that the proposed system shows superior performance regarding the quality of encryption and confidentiality of generated cancelable templates from the original input biometrics. Full article
(This article belongs to the Special Issue Mathematics, Cryptography, Secret Sharing, Information Hiding and Their Applications)
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18 pages, 6821 KiB  
Article
Secure Reversible Data Hiding in Images Based on Linear Prediction and Bit-Plane Slicing
by Maham Nasir, Waqas Jadoon, Iftikhar Ahmed Khan, Nosheen Gul, Sajid Shah, Mohammed ELAffendi and Ammar Muthanna
Mathematics 2022, 10(18), 3311; https://doi.org/10.3390/math10183311 - 12 Sep 2022
Cited by 8 | Viewed by 2656
Abstract
Reversible Data Hiding (RDH) should be secured as per requirements to protect content in open environments such as the cloud and internet. Integrity and undetectability of steganographic images are amongst the main concerns in any RDH scheme. As steganographic encryption using linear prediction [...] Read more.
Reversible Data Hiding (RDH) should be secured as per requirements to protect content in open environments such as the cloud and internet. Integrity and undetectability of steganographic images are amongst the main concerns in any RDH scheme. As steganographic encryption using linear prediction over bit-planes is challenging, so the security and embedding capacity of the existing RDH techniques could not be adequate. Therefore, a new steganographic technique is proposed which provides better security, higher embedding capacity and visual quality to the RDH scheme. In this technique, the cover image is divided into n-bit planes (nBPs) and linear prediction is applied to it. Next, the histogram of the residual nBPs image is taken, and secret data bits are encrypted using the RC4 cryptographic algorithm. To embed the encrypted secret data bits, the histogram shifting process is applied. This is achieved by using peak and zero pairs of residual nBPs images. This scheme provides security to the cover image and hidden data. The proposed RDH scheme is capable of extracting the embedded secret data accurately and recovering the original cover or residual nBPs image. Full article
(This article belongs to the Special Issue Mathematics, Cryptography, Secret Sharing, Information Hiding and Their Applications)
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