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Symmetry
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Symmetries and Symmetry-Breaking in Data Security
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Symmetries and Symmetry-Breaking in Data Security

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3932

Special Issue Editors

Prof. Dr. Na Ren

E-Mail Website
Guest Editor
School of Geographical Sciences, Nanjing Normal University, Nanjing 210023, China
Interests: geographic information security theory; technology and application
Dr. Weitong Chen

E-Mail Website
Guest Editor
School of Information Engineering, Yangzhou University, Yangzhou 225127, China
Interests: data security; AI security

Special Issue Information

Dear Colleagues,

Data security is fundamental to protecting the integrity and reliability of information in our increasingly digital world, ensuring trust and safety for individuals, businesses, and governments alike. Symmetries and symmetry-breaking play significant roles in data security. The study of symmetries and symmetry-breaking provides valuable insights and tools for developing more secure and resilient data protection methods. Recently, many researchers have concentrated on watermarking techniques for data protection, encryption methods to ensure data integrity and confidentiality, secure data transactions, and privacy-preserving computation strategies to enhance data security. Additionally, there is a focus on the dynamic interplay between attacks and defensive measures in data security, emphasizing innovative approaches to mitigate threats. Understanding the concepts of symmetries and symmetry-breaking allows security professionals to anticipate potential vulnerabilities and design methods that are robust against a wide range of threats.

This Special Issue invites contributions exploring the role of symmetries and their disruption in the realm of data security. This issue seeks to address both theoretical and practical aspects of how symmetry principles can enhance or compromise security mechanisms across various domains.

Prof. Dr. Na Ren
Dr. Weitong Chen
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. Symmetry is an international peer-reviewed open access monthly 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 2400 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

  • data security
  • digital watermark
  • data encryption
  • data transactions
  • privacy-preserving computation
  • attack and defense

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 1337 KiB  
Article
Chaotic Real Number Generator with Quantum Wave Equation
by Muharrem Tuncay Gençoğlu, Özgür Karaduman and Fatih Özkaynak
Symmetry 2025, 17(3), 349; https://doi.org/10.3390/sym17030349 - 25 Feb 2025
Viewed by 490
Abstract
Randomness plays a crucial role in numerous applications, with cryptography being one of the most significant areas where its importance is evident. A major challenge in cryptographic applications is designing a reliable key generator that meets stringent security requirements. Existing methods often suffer [...] Read more.
Randomness plays a crucial role in numerous applications, with cryptography being one of the most significant areas where its importance is evident. A major challenge in cryptographic applications is designing a reliable key generator that meets stringent security requirements. Existing methods often suffer from predictability and fail to provide robust randomness, necessitating novel mathematical approaches. In this study, we propose an innovative mathematical framework that integrates quantum wave functions with chaotic systems to enhance the unpredictability and security of random number generation. The proposed approach leverages the inherent uncertainty of quantum mechanics and the dynamic behavior of chaos to generate statistically strong random sequences. The analysis results confirm that the proposed generator successfully passes all standard statistical randomness tests, demonstrating its effectiveness in cryptographic applications. Additionally, we present a practical implementation of the proposed method as an image encryption algorithm, showcasing its potential for real-world information security solutions. The findings suggest that this approach can contribute significantly to secure communication systems, financial transactions, and other domains requiring high-level cryptographic security. Full article
(This article belongs to the Special Issue Symmetries and Symmetry-Breaking in Data Security)
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17 pages, 1472 KiB  
Article
Clean-Label Backdoor Watermarking for Dataset Copyright Protection via Trigger Optimization
by Weitong Chen, Gaoyang Wei, Xin Xu, Yanyan Xu, Haibo Peng and Yingchen She
Symmetry 2024, 16(11), 1494; https://doi.org/10.3390/sym16111494 - 8 Nov 2024
Cited by 1 | Viewed by 987
Abstract
High-quality datasets are essential for training high-performance models, while the process of collection, cleaning, and labeling is costly. As a result, datasets are considered valuable intellectual property. However, when security mechanisms are symmetry-breaking, creating exploitable vulnerabilities, unauthorized use or data leakage can infringe [...] Read more.
High-quality datasets are essential for training high-performance models, while the process of collection, cleaning, and labeling is costly. As a result, datasets are considered valuable intellectual property. However, when security mechanisms are symmetry-breaking, creating exploitable vulnerabilities, unauthorized use or data leakage can infringe on the copyright of dataset owners. In this study, we design a method to mount clean-label dataset watermarking based on trigger optimization, aiming to protect the copyright of the dataset from infringement. We first perform iterative optimization of the trigger based on a surrogate model, with targets class samples guiding the updates. The process ensures that the optimized triggers contain robust feature representations of the watermark target class. A watermarked dataset is obtained by embedding optimized triggers into randomly selected samples from the watermark target class. If an adversary trains a model with the watermarked dataset, our watermark will manipulate the model’s output. By observing the output of the suspect model on samples with triggers, it can be determined whether the model was trained on the watermarked dataset. The experimental results demonstrate that the proposed method exhibits high imperceptibility and strong robustness against pruning and fine-tuning attacks. Compared to existing methods, the proposed method significantly improves effectiveness at very low watermarking rates. Full article
(This article belongs to the Special Issue Symmetries and Symmetry-Breaking in Data Security)
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24 pages, 44119 KiB  
Article
Chebyshev Chaotic Mapping and DWT-SVD-Based Dual Watermarking Scheme for Copyright and Integrity Authentication of Remote Sensing Images
by Jie Zhang, Jinglong Du, Xu Xi and Zihao Yang
Symmetry 2024, 16(8), 969; https://doi.org/10.3390/sym16080969 - 30 Jul 2024
Cited by 2 | Viewed by 1562
Abstract
Symmetries and symmetry-breaking play significant roles in data security. While remote sensing images, being extremely sensitive geospatial data, require protection against tampering or destruction, as well as assurance of the reliability of the data source during application. In view of the increasing complexity [...] Read more.
Symmetries and symmetry-breaking play significant roles in data security. While remote sensing images, being extremely sensitive geospatial data, require protection against tampering or destruction, as well as assurance of the reliability of the data source during application. In view of the increasing complexity of data security of remote sensing images, a single watermark algorithm is no longer adequate to meet the demand of sophisticated applications. Therefore, this study proposes a dual watermarking algorithm that considers both integrity authentication and copyright protection of remote sensing images. The algorithm utilizes Discrete Wavelet Transform (DWT) to decompose remote sensing images, then constructs integrity watermark information by applying Chebyshev mapping to the mean of horizontal and vertical components. This semi-fragile watermark information is embedded into the high-frequency region of DWT using Quantization Index Modulation (QIM). On the other hand, the robust watermarking uses entropy to determine the embedding position within the DWT domain. It combines the stability of Singular Value Decomposition (SVD) and embeds the watermark according to the relationship between the singular values of horizontal, vertical, and high-frequency components. The experiment showed that the proposed watermarking successfully maintains a high level of invisibility even if embedded with dual watermarks. The semi-fragile watermark can accurately identify tampered regions in remote sensing images under conventional image processing. Moreover, the robust watermark exhibits excellent resistance to various attacks such as noise, filtering, compression, panning, rotating, and scaling. Full article
(This article belongs to the Special Issue Symmetries and Symmetry-Breaking in Data Security)
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