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Public Key Cryptography and Privacy Protection
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Public Key Cryptography and Privacy Protection

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Information Security and Privacy".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 8883

Editors

Dr. I-Pin Chang

E-Mail Website
Guest Editor
Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
Interests: database management system; system development; information security; statistical analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tian-Fu Lee

E-Mail Website
Guest Editor
Department of Medical Informatics, Tzu Chi University, Hualien 970374, Taiwan
Interests: cryptography; medical information security; wireless network; network security; sensor networks and HIPAA privacy/security regulations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Public key cryptography (PKC) plays a pivotal role in securing digital communications and safeguarding privacy in our increasingly interconnected world. PKC enables secure communication, authentication, and data integrity by leveraging mathematical principles and asymmetric key pairs. Privacy protection, as a critical domain of cybersecurity, relies heavily on advancements in cryptographic methods to ensure confidentiality, data security, and trust in diverse applications.

This Special Issue seeks innovative contributions in the form of high-quality, unpublished, and in-depth fundamental research that aims to address the existing technical challenges and propose novel solutions in the domain of public key cryptography and privacy protection.

The goal of this Special Issue is to explore current methods, architectures, and frameworks for PKC and privacy protection, as well as to integrate emerging technologies. Emphasis will be placed on performance evaluation, security analysis, and comparison with existing approaches to advance the state of the art in cryptography and privacy-enhancing technologies. This Special Issue encourages both theoretical and experimental studies in these fields. Additionally, high-quality review and survey papers are welcome. Academic researchers, developers, and industry practitioners are invited to contribute papers to this Special Issue.

Topics of Interest include (but are not limited to) one or more of the following:

  • Cryptography;
  • Public key cryptography;
  • Key management systems and strategies;
  • Cryptographic applications for privacy protection;
  • Privacy-preserving techniques for data sharing and computation;
  • Secure multi-party computation;
  • Cryptographic frameworks for IoT, cloud, and edge computing;
  • Blockchain and decentralized systems security;
  • Privacy and trust in AI-driven systems;
  • Threat modeling and countermeasures for PKC-based systems.

We encourage the submission of original research that addresses fundamental challenges, demonstrates practical applications, or proposes novel solutions in the field of public key cryptography and privacy protection. Contributions that advance theoretical understanding, provide innovative experimental results, or offer high-quality reviews of state-of-the-art developments are highly valued.

We look forward to your submissions and contributions to advancing the fields of public key cryptography and privacy protection.

Dr. I-Pin Chang
Prof. Dr. Tian-Fu Lee
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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 1800 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

  • cryptography
  • public key cryptography
  • privacy protection
  • information security
  • network security
  • sensor networks

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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

25 pages, 1516 KB  
Article
Comparative Benchmarking of Deep Learning Architectures for Detecting Adversarial Attacks on Large Language Models
by Oleksandr Kushnerov, Ruslan Shevchuk, Serhii Yevseiev and Mikołaj Karpiński
Information 2026, 17(2), 155; https://doi.org/10.3390/info17020155 - 4 Feb 2026
Cited by 1 | Viewed by 1902
Abstract
The rapid adoption of large language models (LLMs) in corporate and governmental systems has raised critical security concerns, particularly prompt injection attacks exploiting LLMs’ inability to differentiate control instructions from untrusted user inputs. This study systematically benchmarks neural network architectures for malicious prompt [...] Read more.
The rapid adoption of large language models (LLMs) in corporate and governmental systems has raised critical security concerns, particularly prompt injection attacks exploiting LLMs’ inability to differentiate control instructions from untrusted user inputs. This study systematically benchmarks neural network architectures for malicious prompt detection, emphasizing robustness against character-level adversarial perturbations—an aspect that remains comparatively underemphasized in the specific context of prompt-injection detection despite its established significance in general adversarial NLP. Using the Malicious Prompt Detection Dataset (MPDD) containing 39,234 labeled instances, eight architectures—Dense DNN, CNN, BiLSTM, BiGRU, Transformer, ResNet, and character-level variants of CNN and BiLSTM—were evaluated based on standard performance metrics (accuracy, F1-score, and AUC-ROC), adversarial robustness coefficients against spacing and homoglyph perturbations, and inference latency. Results indicate that the word-level 3_Word_BiLSTM achieved the highest performance on clean samples (accuracy = 0.9681, F1 = 0.9681), whereas the Transformer exhibited lower accuracy (0.9190) and significant vulnerability to spacing attacks (adversarial robustness ρspacing=0.61). Conversely, the Character-level BiLSTM demonstrated superior resilience (ρspacing=1.0, ρhomoglyph=0.98), maintaining high accuracy (0.9599) and generalization on external datasets with only 2–4% performance decay. These findings highlight that character-level representations provide intrinsic robustness against obfuscation attacks, suggesting Char_BiLSTM as a reliable component in defense-in-depth strategies for LLM-integrated systems. Full article
(This article belongs to the Special Issue Public Key Cryptography and Privacy Protection)
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20 pages, 1458 KB  
Article
FedRazor: Two-Stage Federated Unlearning via Representation Divergence and Gradient Conflict Trimming
by Yanxin Hu, Xiaoman Liu, Yan Huang, Junjie Pang, Chao Cheng and Gang Liu
Information 2026, 17(2), 146; https://doi.org/10.3390/info17020146 - 2 Feb 2026
Cited by 1 | Viewed by 1151
Abstract
Federated unlearning removes a client’s influence from a trained federated model without full retraining, which is required by data deletion regulations but remains difficult due to gradient coupling and recovery instability. Existing methods often rely on historical training records or suffer from severe [...] Read more.
Federated unlearning removes a client’s influence from a trained federated model without full retraining, which is required by data deletion regulations but remains difficult due to gradient coupling and recovery instability. Existing methods often rely on historical training records or suffer from severe utility degradation and model reverting after recovery. We propose FedRazor, a two-stage federated unlearning framework that achieves stable client-level unlearning through representation divergence and gradient direction control. In Stage I, FedRazor weakens dependence on forgotten data using two complementary objectives. A Divergence-Smoothing Loss reduces prediction confidence on forgotten labels, while a Feature Mean Divergence loss pushes forgotten representations away from the retained feature center. To protect retained performance, we introduce PCGrad Razor, which trims gradient components that conflict with retained gradients during aggregation. This stage produces an intermediate unlearned model without storing historical updates. In Stage II, FedRazor restores retained utility using directional gradient trimming. Gradients aligned with the unlearning displacement direction are removed, preventing forgotten information from re-entering the model during recovery. Experiments on MNIST, CIFAR-10, and CIFAR-100 under IID and non-IID settings show that FedRazor consistently reduces attack success rate to near zero while preserving retained accuracy. On CIFAR-10 Pat-50, FedRazor achieves ASR = 0.026 with retained accuracy 0.659 after post-training, outperforming strong baselines in stability and unlearning robustness. Full article
(This article belongs to the Special Issue Public Key Cryptography and Privacy Protection)
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20 pages, 437 KB  
Article
Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber)
by Qaiser Khan, Sourav Purification and Sang-Yoon Chang
Information 2025, 16(7), 617; https://doi.org/10.3390/info16070617 - 19 Jul 2025
Cited by 8 | Viewed by 4902
Abstract
5G addresses user privacy concerns in cellular networking by encrypting a subscriber identifier with elliptic-curve-based encryption and then transmitting it as ciphertext known as a Subscriber Concealed Identifier (SUCI). However, an adversary equipped with a quantum computer can break a discrete-logarithm-based elliptic curve [...] Read more.
5G addresses user privacy concerns in cellular networking by encrypting a subscriber identifier with elliptic-curve-based encryption and then transmitting it as ciphertext known as a Subscriber Concealed Identifier (SUCI). However, an adversary equipped with a quantum computer can break a discrete-logarithm-based elliptic curve algorithm. Consequently, the user privacy in 5G is at stake against quantum attacks. In this paper, we study the incorporation of the post-quantum ciphers in the SUCI calculation both at the user equipment and at the core network, which involves the shared-key exchange and then using the resulting key for the ID encryption. We experiment on different hardware platforms to analyze the PQC key exchange and encryption using NIST-standardized CRYSTALS-Kyber (which is now called an ML-KEM after the standardization selection by NIST). Our analyses focus on the performances and compare the Kyber-based key exchange and encryption with the current (pre-quantum) elliptic curve Diffie–Hellman (ECDH). The performance analyses are critical because mobile networking involves resource-limited and battery-operating mobile devices. We measure and analyze not only the time and CPU-processing performances but also the energy and power performances. Our analyses show that Kyber-512 is the most efficient and even has better performance (i.e., faster computations and lower energy consumption) than ECDH. Full article
(This article belongs to the Special Issue Public Key Cryptography and Privacy Protection)
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