Symmetry/Asymmetry in Cryptographic Approach for Privacy-Preserving Data Transmission and Data Analysis
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 241
Special Issue Editors
Interests: machine learning; cybersecurity; data analysis
Interests: mechanical engineering; biomedical engineering; medical image processing; biomechanics
Interests: AI safety and security; blockchain; IoT security; data privacy; cryptology; OT security
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Special Issue Information
Dear Colleagues,
Privacy-preserving technologies (PPT) are methods, tools, and protocols designed to protect sensitive data while still enabling useful computations or insights. These techniques aim to ensure that personal or confidential information is not exposed, misused, or leaked during processing, storage, or sharing. Common approaches include data anonymisation, differential privacy (DP), access control, and secure multi-party computation (SMPC). In all PPTs, cryptography plays a key role, providing guarantees of data confidentiality, integrity, and authenticity. Indeed, techniques such as homomorphic encryption (HE), Secret Sharing Schemes (SSS), SMPC, and zero-knowledge proofs (ZKP) are widely used and fundamental in many applications. A central challenge lies in adapting these existing technologies to achieve higher security while remaining usable on resource-constrained devices. At the same time, it is essential to maintain reliability with minimal performance impact, ensuring that the privacy risks associated with data and operations are matched by an appropriate level of protection through optimal techniques. Furthermore, recent advances in Generative AI (GenAI) and Large Language Models (LLMs) have introduced new privacy challenges, as current architectures often process and transmit sensitive data without robust cryptographic protection or verifiable privacy guarantees. This gap calls for the integration of symmetric and asymmetric cryptographic mechanisms into AI-driven data transmission and analysis pipelines.
We are pleased to invite you to submit original research articles and reviews to this Special Issue, which aims to highlight the latest developments and applications of cryptographic technologies for enhancing data privacy. This Special Issue seeks contributions that explore innovative approaches, practical implementations, and theoretical advancements in cryptography, particularly those that address challenges in sectors such as industry, IoT communication, healthcare, finance, biometrics, cybersecurity, and networks (5G/6G). In addition, the Special Issue promotes analyses of how current PPTs are integrated into federated learning models to enhance privacy and security without centralising sensitive data. Finally, works that analyse and propose to address potential vulnerabilities to post-quantum cryptography (PQC) attacks are highly encouraged.
We look forward to receiving your contributions.
Dr. Francesco Zola
Dr. Jan Lukas Bruse
Prof. Dr. Kuo-Hui Yeh
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-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
- privacy-preserving technology
- privacy-enhancing techniques
- federated learning
- cross-border federated computing
- cryptography
- post-quantum cryptography
- financial applications
- healthcare applications
- privacy-preserving GenAI/LLM/Agentic AI applications
- cybersecurity
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