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Symmetry
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Applications Based on Symmetry in Cryptography and Information Security
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Applications Based on Symmetry in Cryptography and Information Security

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 6580

Special Issue Editor

Dr. Yanwei Zhou

E-Mail Website
Guest Editor
School of Computer Science, Shaanxi Normal University, Xi’an 710062, China
Interests: cryptography; information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cryptography and information security are crucial fields that have been gaining significant attention due to their importance in protecting sensitive data and ensuring secure communications. They involve the use of various techniques and algorithms to encrypt and decrypt information and safeguard it from unauthorized access and attacks. This field is essential for maintaining information confidentiality, integrity, and availability in various sectors, including finance, healthcare, and government. Furthermore, symmetry plays an important role in cryptography information security.

This Special Issue aims to bring together researchers and practitioners from diverse disciplines to share their latest findings and advancements in cryptography and information security. We are particularly interested in submissions that explore novel approaches and developments in this field, including but not limited to secure computation, cryptography theory and algorithms, cryptographic engineering and applications, and intelligent security and privacy computing. Potential areas of interest include the development of new encryption algorithms, techniques for secure data transmission, methods for detecting and preventing cyber attacks, and approaches to ensuring the privacy and security of personal data.

We welcome submissions on topics including, but not limited to, the following:

  • Cryptographic protocols and secure communication systems;
  • Digital signatures and authentication methods;
  • Leakage-resilient cryptographic algorithms and their implementations;
  • Public-key encryption techniques and their security properties;
  • Cryptographic primitives for data integrity and authentication;
  • Functional encryption for secure data processing and analysis;
  • Security and privacy in cloud computing and big data analytics.

All submissions will undergo a rigorous peer-review process to ensure their quality and originality. Accepted papers will be published and accessible to all readers.

Dr. Yanwei Zhou
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • cryptographic protocols and secure communication systems
  • digital signatures and authentication methods
  • leakage-resilient cryptographic algorithms and their implementations
  • public-key encryption techniques and their security properties
  • cryptographic primitives for data integrity and authentication
  • functional encryption for secure data processing and analysis
  • security and privacy in cloud computing and big data analytics.

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

Published Papers (4 papers)

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Research

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24 pages, 8692 KB  
Article
APDP-FL: Personalized Federated Learning Based on Adaptive Differential Privacy
by Feng Guo, Ruoxu Wang, Jiuru Wang, Chen Yang, Zhuo Liu and Hongtao Li
Symmetry 2025, 17(12), 2023; https://doi.org/10.3390/sym17122023 - 24 Nov 2025
Cited by 1 | Viewed by 1261
Abstract
Frequent gradient exchange and heterogeneous data distribution in federated learning can lead to serious privacy leakage risks. Traditional privacy-preserving strategies fail to meet the personalized privacy needs from different users and may cause a decrease in model accuracy and convergence difficulties. The symmetry [...] Read more.
Frequent gradient exchange and heterogeneous data distribution in federated learning can lead to serious privacy leakage risks. Traditional privacy-preserving strategies fail to meet the personalized privacy needs from different users and may cause a decrease in model accuracy and convergence difficulties. The symmetry of federated learning may lead to the insufficiency of contribution evaluation mechanisms in protecting the privacy of sensitive data holders. However, federated learning avoids the risk of privacy leakage caused by data centralization because the raw data is always stored on the local device during the training process, and only encrypted model parameters or gradient updates are exchanged. To address these issues, this paper proposes an adaptive personalized differential privacy federated learning scheme APDP-FL. First, we propose an adaptive noise addition method that scores each round of training based on the parameters generated during training and dynamically adjusts the noise level for the next round. This method adds larger noise scales in the early stages of training, consuming less privacy budget, and gradually reduces noise addition during training to accelerate model convergence. Second, we design a personalized privacy protection strategy that adds noise tailored to individual needs for participating clients based on their privacy preferences. This solves the problem of insufficient or excessive privacy protection for some participants due to identical privacy budget sets for all clients, achieving personalized privacy protection for clients. Finally, we conduct extensive experimental simulations, comparisons, and analyses on three real federated datasets, MNIST, FMNIST, and CIFAR-10, verifying the advantages of APDP-FL in terms of privacy protection, model accuracy, and convergence speed. Full article
(This article belongs to the Special Issue Applications Based on Symmetry in Cryptography and Information Security)
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28 pages, 1103 KB  
Article
An Efficient and Effective Model for Preserving Privacy Data in Location-Based Graphs
by Surapon Riyana and Nattapon Harnsamut
Symmetry 2025, 17(10), 1772; https://doi.org/10.3390/sym17101772 - 21 Oct 2025
Viewed by 1106
Abstract
Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry [...] Read more.
Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry in recurring behavior mobility patterns and asymmetry in irregular movement mobility patterns in sensitive locations collectively expose highly identifiable information, resulting in re-identification risks, trajectory disclosure, and location inference. In response, several privacy preservation models have been proposed, including k-anonymity, l-diversity, t-closeness, LKC-privacy, differential privacy, and location-based approaches. However, these models still exhibit privacy issues, including sensitive location inference (e.g., hospitals, pawnshops, prisons, safe houses), disclosure from duplicate trajectories revealing sensitive places, and the re-identification of unique locations such as homes, condominiums, and offices. Efforts to address these issues often lead to utility loss and computational complexity. To overcome these limitations, we propose a new (ξ, ϵ)-privacy model that combines data generalization and suppression with sliding windows and R-Tree structures, where sliding windows partition large trajectory graphs into simplified subgraphs, R-Trees provide hierarchical indexing for spatial generalization, and suppression removes highly identifiable locations. The model addresses both symmetry and asymmetry in mobility patterns by balancing generalization and suppression to protect privacy while maintaining data utility. Symmetry-driven mechanisms that enhance resistance to inference attacks and support data confidentiality, integrity, and availability are core requirements of cryptography and information security. An experimental evaluation on the City80k and Metro100k datasets confirms that the (ξ, ϵ)-privacy model addresses privacy issues with reduced utility loss and efficient scalability, while validating robustness through relative error across query types in diverse analytical scenarios. The findings provide evidence of the model’s practicality for large-scale location data, confirming its relevance to secure computation, data protection, and information security applications. Full article
(This article belongs to the Special Issue Applications Based on Symmetry in Cryptography and Information Security)
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44 pages, 852 KB  
Article
An Intelligent Risk Assessment Methodology for the Full Lifecycle Security of Data
by Jinhui Liu, Tianyi Han, Jingjing Zhao, Dejun Mu, Huan Liu and Bo Tang
Symmetry 2025, 17(6), 820; https://doi.org/10.3390/sym17060820 - 24 May 2025
Cited by 1 | Viewed by 1885
Abstract
With the development of Internet of Things and artificial intelligence, large amounts of data exist in our daily life. In view of the limitations in current data security risk assessment research, this paper puts forward an intelligent data security risk assessment method based [...] Read more.
With the development of Internet of Things and artificial intelligence, large amounts of data exist in our daily life. In view of the limitations in current data security risk assessment research, this paper puts forward an intelligent data security risk assessment method based on an attention mechanism that spans the entire data lifecycle. The initial step involves formulating a security-risk evaluation index that spans all phases of the data lifecycle. By constructing a symmetric mapping of subjective and objective weights using the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM), both expert judgment and objective data are comprehensively considered to scientifically determine the weights of various risk indicators, thereby enhancing the rationality and objectivity of the assessment framework. Next, the fuzzy comprehensive evaluation method is used to label the risk level of the data, providing an essential basis for subsequent model training. Finally, leveraging the structurally symmetric attention mechanism, we design and train a neural network model for data security risk assessment, enabling automatic capture of complex features and nonlinear correlations within the data for more precise and accurate risk evaluations. The proposed risk assessment approach embodies symmetry in both the determination of indicator weights and the design of the neural network architecture. Experimental results indicate that our proposed method achieves high assessment accuracy and stability, effectively adapts to data security risk environments, and offers a feasible intelligent decision aid tool for data security management. Full article
(This article belongs to the Special Issue Applications Based on Symmetry in Cryptography and Information Security)
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Review

Jump to: Research

39 pages, 1414 KB  
Review
Differential Cryptanalysis of Block Ciphers Through the Lens of Symmetry: A Review
by Lei Zhang, Yvxuan Wu, Yaxuan Wen, Chaoen Xiao, Ding Ding and Quanrun Lv
Symmetry 2026, 18(1), 8; https://doi.org/10.3390/sym18010008 - 19 Dec 2025
Viewed by 1403
Abstract
Differential cryptanalysis is a fundamental technique in symmetric-key cryptanalysis. While the existing literature and several surveys have separately addressed classical differential attacks, deep learning-assisted cryptanalysis, and quantum-related attacks, a systematic presentation that enables cross-paradigm comparison, lineage mapping, and methodological evaluation is still lacking. [...] Read more.
Differential cryptanalysis is a fundamental technique in symmetric-key cryptanalysis. While the existing literature and several surveys have separately addressed classical differential attacks, deep learning-assisted cryptanalysis, and quantum-related attacks, a systematic presentation that enables cross-paradigm comparison, lineage mapping, and methodological evaluation is still lacking. To address this gap, this paper organizes its analysis along these three evolutionary threads. First, we trace the evolutionary trajectory of classical differential cryptanalysis. We distill eight representative technical pathways and group them into four categories based on mechanistic characteristics to facilitate cross-comparison. Second, we classify the integration of deep learning with differential cryptanalysis into two distinct paradigms: “deep learning-assisted” and “deep learning-based.” We discuss their roles in feature extraction, trail search, and key-recovery (KR) while also reviewing reproducible evidence, common limitations, and empirical challenges. Third, we survey quantum computing-based approaches. In light of current algorithms and hardware constraints, we examine their potential speedups and applicability boundaries in characteristic search and KR. Our synthesis of existing work reveals distinct capability boundaries for each paradigm and identifies key challenges in their practical application. This paper offers a structured comparative framework, aiming to serve as a reusable reference and baseline for future research. Full article
(This article belongs to the Special Issue Applications Based on Symmetry in Cryptography and Information Security)
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