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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) belonging to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 2700

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-anonymized 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 (4 papers)

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Research

24 pages, 3034 KB  
Article
An Explainable CS-Mitigation Triangular (ECSMT) Framework to Secure Graph Neural Networks
by Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen and Wen-Chao Yang
Electronics 2026, 15(13), 2967; https://doi.org/10.3390/electronics15132967 - 7 Jul 2026
Viewed by 343
Abstract
This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, [...] Read more.
This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary AIDS benchmark. Cross-domain testing reveals that defensive efficacy is strongly constrained by dataset characteristics: small-scale datasets such as MUTAG suffer from persistent trigger concentration, while complex graph manifolds such as PROTEINS exhibit high levels of topological noise. Furthermore, mapping these technical outcomes into an enterprise asset framework yields a 61% expenditure compression at critical technological feeder locations and a 98.93% reduction in total systemic loss. This study indicates that the proposed triangular mitigation strategy offers a valuable, scalable blueprint for enhancing the technical resilience and prognostic economic modeling of critical infrastructure networks. Full article
(This article belongs to the Special Issue Secure and Privacy-Enhanced Data Sharing)
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39 pages, 3618 KB  
Article
Efficient Authenticated Fine-Grained Access Engine for Encrypted Data in Mobile Edge Cloud
by Zhishuo Zhang, Jianding Guo, Caixing Shao, Wen Huang and Shijie Zhou
Electronics 2026, 15(13), 2933; https://doi.org/10.3390/electronics15132933 - 4 Jul 2026
Viewed by 333
Abstract
Fine-grained, authenticated, traceable, and efficient encrypted access control is indispensable for secure data sharing in mobile edge cloud networks, especially for resource-constrained data requesters. Despite the prevalence of outsourced ciphertext-policy attribute-based encryption (CP-ABE) solutions, existing schemes still suffer from critical practical limitations. First, [...] Read more.
Fine-grained, authenticated, traceable, and efficient encrypted access control is indispensable for secure data sharing in mobile edge cloud networks, especially for resource-constrained data requesters. Despite the prevalence of outsourced ciphertext-policy attribute-based encryption (CP-ABE) solutions, existing schemes still suffer from critical practical limitations. First, requester-side transformation keys are typically unverified prior to computationally expensive outsourced decryption operations. Second, commitment-based verification mechanisms fail to validate the identity of data publishers. Third, the online computational overhead scales linearly with either the requester attribute set or the policy-matching set, severely degrading practical efficiency. To address these issues, this paper proposes ePoFSC, a novel policy-oriented functional signcryption scheme for authenticated encrypted data sharing in mobile edge cloud scenarios. The proposed ePoFSC scheme integrates pre-auditing and caching mechanisms for requester trapdoors before online access requests, enabling constant-time operations for request generation, request verification, and request header construction independent of requester attribute scale. In the outsourced decryption phase, ePoFSC offloads all costly pairing and exponentiation operations with constant computational complexity, leaving only lightweight policy-dependent group multiplications for terminal requesters. Furthermore, ePoFSC tightly couples decryption verification with publisher authentication and requester traceability to realize comprehensive access accountability. Rigorous security analysis formally validates the confidentiality, publisher-side unforgeability, and requester traceability of the proposed scheme. Extensive experimental evaluations on the BLS12-381 curve verify that ePoFSC achieves prominent performance superiority over existing state-of-the-art schemes in both the encryption and data recovery phases. Full article
(This article belongs to the Special Issue Secure and Privacy-Enhanced Data Sharing)
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44 pages, 2947 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
Viewed by 429
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)
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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 764
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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