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Electronics
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Secure and Privacy-Enhanced Data Sharing
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Secure and Privacy-Enhanced Data Sharing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 810

Special Issue Editors

Prof. Dr. Chuan Zhao

E-Mail Website
Guest Editor
School of Information Science and Engineering, University of Jinan, Jinan 250022, China
Interests: privacy computing; blockchain; applied cryptography; artificial intelligence security
Special Issues, Collections and Topics in MDPI journals
Dr. Shengnan Zhao

E-Mail Website
Guest Editor
Quan Cheng Laboratory, Jinan 250103, China
Interests: applied cryptography; private set operations

Special Issue Information

Dear Colleagues,

With the widespread adoption of data-driven technologies such as the Internet of Things, edge computing, and large language models, data sharing has become an indispensable cornerstone for cross-sector collaboration, scientific innovation, and industrial digital transformation. However, the conflict between the need for open data flow and the necessity of protecting sensitive information has led to key issues such as data breaches, privacy violations, and unauthorized uses. Against this backdrop, secure and privacy-enhanced data sharing technologies have emerged as a critical research focus, driving continuous breakthroughs in balancing data value exploitation and privacy security protection to meet the evolving needs of trusted data ecosystems. The scope of this Special Issue, “Secure and Privacy-Enhanced Data Sharing”, covers theoretical foundations, practical mechanisms, and real-world implementations of secure data sharing systems. In this Special Issue, we aim to highlight cutting-edge research and developments that enhance the security and privacy of data sharing across domains such as healthcare, finance, industrial IoT, and cross-organizational collaborations.

We welcome the submission of original research and review articles that systematically address key challenges in secure and privacy-focused data sharing. Topics of interest range from novel cryptographic protocols and blockchain-based access control to differential privacy techniques, usability studies, and practical deployment experiences. Survey papers regarding the state-of-the-art solutions to these topics are also welcome.

Potential topics include, but are not limited to, the following:

  • Secure data sharing in large language models;
  • Data ownership and user-centric sharing models;
  • Consent management and transparent data usage;
  • Blockchain and decentralized data sharing systems;
  • Cross-domain and cross-organizational data sharing;
  • Cloud computing, database, and computing infrastructure security;
  • Usable security and human factors in data sharing;
  • Post-quantum cryptography;
  • Homomorphic encryption for data sharing;
  • Real-world implementations and case studies.

We look forward to receiving your contributions.

Prof. Dr. Chuan Zhao
Dr. Shengnan Zhao
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. Electronics 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 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 sharing
  • privacy-enhanced technologies
  • cyberspace security
  • information security
  • data privacy
  • security and privacy applications
  • cryptography

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Published Papers (2 papers)

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44 pages, 2939 KB  
Article
RUIP-BA: Renewable, Unlinkable, and Irreversible Privacy-Preserving Behavioral Authentication via Random Projection and Local Differential Privacy
by Md Morshedul Islam, Khondokar Fida Hasan, Wali Mohammad Abdullah and Baidya Nath Saha
Electronics 2026, 15(11), 2287; https://doi.org/10.3390/electronics15112287 - 25 May 2026
Abstract
Behavioral authentication (BA) systems verify user identity claims based on unique behavioral characteristics using machine learning (ML)-based classifiers trained on user behavioral profiles. Although effective, ML-based BA systems face serious privacy threats, including profile inference and reconstruction attacks. This paper presents RUIP-BA (Renewable, [...] Read more.
Behavioral authentication (BA) systems verify user identity claims based on unique behavioral characteristics using machine learning (ML)-based classifiers trained on user behavioral profiles. Although effective, ML-based BA systems face serious privacy threats, including profile inference and reconstruction attacks. This paper presents RUIP-BA (Renewable, Unlinkable, and Irreversible Privacy-Preserving Behavioral Authentication), a non-cryptographic framework designed for settings where computational resources may be limited. Random Projection (RP) maps behavioral profiles into lower-dimensional protected templates while approximately preserving utility-relevant geometry, and local Differential Privacy (DP) injects calibrated stochastic perturbations to provide formal privacy protection. The proposed design jointly targets the ISO/IEC 24745 requirements of renewability, unlinkability, and irreversibility. We provide complete algorithmic realizations for enrollment, verification, template renewal, unlinkability testing, and GAN-based adversarial privacy evaluation. We also introduce rigorous formal privacy derivations and proofs under explicit assumptions, including formal security games, information-theoretic theorem-level guarantees, Cramér–Rao lower bounds for irreversibility, full Jensen–Shannon divergence derivations for unlinkability, and a GAN Nash-equilibrium attack bound. Comprehensive dimensionality ablation across all three modalities confirms robust utility at compact template sizes, and an expanded analysis of the privacy–utility trade-off under varying ϵ values is provided. Experiments on voice, swipe, and drawing datasets show authentication accuracy above 96% while sharply limiting feature recoverability under strong GAN-based attacks. All reported FAR/FRR figures are single-session best-case estimates; cross-session longitudinal evaluation remains future work. RUIP-BA provides a scalable, mathematically grounded, and deployment-ready privacy-preserving BA solution. Full article
(This article belongs to the Special Issue Secure and Privacy-Enhanced Data Sharing)
17 pages, 5449 KB  
Article
A Device-Centric Research of Power Side-Channel in FPGAs
by Kaishun Zhang, Changhao Wang and Tao Su
Electronics 2026, 15(8), 1546; https://doi.org/10.3390/electronics15081546 - 8 Apr 2026
Viewed by 447
Abstract
As a widely used computing substrate, the side-channel security of FPGAs has attracted considerable attention, yet a systematic understanding of how FPGA device types contribute to exploitable leakage remains limited. This work presents a device-centric evaluation that maps an S-box-like function onto common [...] Read more.
As a widely used computing substrate, the side-channel security of FPGAs has attracted considerable attention, yet a systematic understanding of how FPGA device types contribute to exploitable leakage remains limited. This work presents a device-centric evaluation that maps an S-box-like function onto common FPGA primitives, including look-up table (LUT), flip-flop (FF), block RAM (BRAM), and distributed RAM (LUTRAM), and assesses Correlation Power Analysis (CPA) outcomes under the Hamming Weight (HW) and Hamming Distance (HD) power models. The results show pronounced leakage differences across device types: FF- and BRAM-based implementations exhibit substantially stronger leakage than LUT- and LUTRAM-based ones, and they frequently achieve GE=0 in our configurations, while the HD model is generally more effective than the HW model in the performed CPA evaluations. Notably, FF-, BRAM-, and LUTRAM-based implementations can already be breakable starting from one instance under the HD model in our device-level tests, indicating that exploitable leakage may manifest in real FPGA applications. These device-level observations are further validated on a practical cipher by analyzing two SM4 encryption modules that differ only in the S-box implementation style; the BRAM-based design shows significantly stronger leakage than the LUT-based design, achieving GE=2.58 versus GE=78.3 at 10,000 traces. This work highlights the critical role of device selection and implementation style in FPGA side-channel security, and it provides practical insights for designing secure FPGA applications against power side-channel analysis. Full article
(This article belongs to the Special Issue Secure and Privacy-Enhanced Data Sharing)
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