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Information
Special Issues
Privacy-Preserving Data Analytics and Secure Computation
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Privacy-Preserving Data Analytics and Secure Computation

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2730

Special Issue Editors

Prof. Dr. Xiaofeng Chen

E-Mail Website
Guest Editor
School of Cyber Engineering, Xidian University, Xi'an 710071, China
Interests: cryptography; cloud computing secuirty; network security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Elisa Bertino

E-Mail Website
Guest Editor
Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
Interests: security of IoT; sensors; embedded systems; and drones; digital identity management; data security and privacy on the cloud; privacy of mobile devices; data trustworthiness
Dr. Fuchun Guo

E-Mail Website
Guest Editor
Institute of Cybersecurity and Cryptology, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: applied cryptography; attribute-based encryption; outsourced decryption; verifiable; cloud computing; authorized client
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Willy Susilo

E-Mail Website
Guest Editor
School of Computer Science and Software Engineering, University of Wollongong, Northfields Avenue, Wollongong, NSW 2522, Australia
Interests: cryptography; computer security; design of signature schemes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

This Special Issue is derived from the DSPP 2025, and it focuses on the discussion on the latest theory, algorithms, applications, and emergingtopics on data security and privacy protection. It aims to showcase extended versions of high-quality conference papers and invite submissions from authors outside the conference whose work aligns with the scope of this Special Issue.

As this Special Issue is associated with a conference, the authors of invited papers should note that the final submitted manuscript must contain a minimum of 50% new content and must not exceed 30% of the original conference proceedings.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Applied Cryptography
  • Authentication, Access Control, and Authorization
  • Adversarial Machine Learning
  • Attacks on Security and Privacy
  • Big Data Security
  • Big Data Protection, Integrity and Privacy
  • Blockchain and Related Technologies
  • Cyber Security
  • Cryptography
  • Database Security
  • Data Encryption Applications
  • Data Mining Security
  • Formal Methods for Security and Privacy
  • Metrics for Security and Privacy
  • Network security and Malware
  • Privacy and Trust
  • Privacy Preserving/Enhancing Technologies
  • Security of Deep Learning Systems
  • Security and Privacy in Cloud
  • Security and privacy Policies
  • Usable Security and Privacy

Prof. Dr. Xiaofeng Chen
Prof. Dr. Elisa Bertino
Dr. Fuchun Guo
Prof. Dr. Willy Susilo
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. 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

  • data security
  • privacy protection
  • applied cryptography
  • cyber security
  • big data protection
  • data encryption

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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

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Research

37 pages, 11359 KB  
Article
Privacy-Enhanced Stable Federated Learning for Statistically Heterogeneous Geospatial Data
by Yiqi Sun, Keer Zhang, Chenxu Liu, Hezheng Lan and Hong Lei
Information 2026, 17(5), 404; https://doi.org/10.3390/info17050404 - 24 Apr 2026
Viewed by 81
Abstract
To address statistical heterogeneity and update-level privacy risks in federated learning for geospatial data, this paper proposes a hierarchically decoupled collaborative framework that integrates client-side privacy perturbation with server-side consistency-aware aggregation, while incorporating governance as a system-level support module. Under strong non-IID conditions, [...] Read more.
To address statistical heterogeneity and update-level privacy risks in federated learning for geospatial data, this paper proposes a hierarchically decoupled collaborative framework that integrates client-side privacy perturbation with server-side consistency-aware aggregation, while incorporating governance as a system-level support module. Under strong non-IID conditions, the proposed soft-weight aggregation strategy mitigates update mismatch and improves convergence stability without hard filtering legitimate but distributionally shifted client contributions. Meanwhile, the risk-aware perturbation mechanism adaptively adjusts clipping and noise strength across clients to better balance privacy protection and model utility. An on-chain governance and off-chain training coordination mechanism is further introduced to support auditable and traceable collaboration without interfering with the main optimization process. Experimental results on EuroSAT_RGB with ResNet-18 show that the proposed design achieves more stable training and better overall performance than the compared baselines, especially under severe heterogeneity. These findings highlight the value of jointly considering privacy-aware perturbation and consistency-aware aggregation for improving training stability and preserving utility in geospatial federated learning under statistically heterogeneous settings. Full article
(This article belongs to the Special Issue Privacy-Preserving Data Analytics and Secure Computation)
40 pages, 15903 KB  
Article
A Unified Clustering-Based Anonymization for Privacy-Preserving Data Publishing with Multidimensional Privacy Quantification
by Anselme Herman Eyeleko, Tao Feng and Yan Yan
Information 2026, 17(3), 302; https://doi.org/10.3390/info17030302 - 20 Mar 2026
Viewed by 302
Abstract
As widely adopted privacy models in privacy-preserving data publishing (PPDP), k-anonymity and ℓ-diversity have been extensively studied by researchers to enable the release of useful information while preserving data privacy. However, existing methods suffer from several limitations. They often rely on [...] Read more.
As widely adopted privacy models in privacy-preserving data publishing (PPDP), k-anonymity and ℓ-diversity have been extensively studied by researchers to enable the release of useful information while preserving data privacy. However, existing methods suffer from several limitations. They often rely on single-dimensional privacy models and lack unified metrics for accurately quantifying privacy leakages. Many approaches overlook the impact of semantic similarity and adversarial prior and posterior beliefs among sensitive attributes and frequently employ suboptimal similarity measures that fail to account for the heterogeneous nature of quasi-identifiers, thereby degrading both privacy protection and data utility. To address these challenges, this paper proposes CAMDP, a unified clustering-based anonymization method for privacy-preserving data publishing with multidimensional privacy quantification. CAMDP constructs equivalence classes that satisfy k-anonymity while simultaneously enhancing sensitive attribute diversity, reducing semantic similarity, and limiting divergence between prior and posterior adversarial beliefs. A unified multidimensional metric is introduced to jointly quantify privacy leakage and information loss, guiding the anonymization process. Additionally, a similarity-aware distance metric tailored to mixed-type quasi-identifiers is employed to reduce information loss. Experimental results on three benchmark datasets, Adult, Careplans, and Airline, demonstrate that CAMDP consistently outperforms state-of-the-art methods. Across all tested configurations, CAMDP achieves the lowest average privacy leakage (0.1235, 0.0795, and 0.1855, respectively), lower average information loss (0.626, 0.636, and 0.60, respectively), and the lowest average intra-cluster dissimilarity (0.586, 0.635, and 0.573, respectively), while maintaining competitive execution time across the three datasets. Full article
(This article belongs to the Special Issue Privacy-Preserving Data Analytics and Secure Computation)
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17 pages, 468 KB  
Article
A Traceable Ring Signcryption Scheme Based on SM9 for Privacy Protection
by Liang Qiao, Xuefeng Zhang and Beibei Li
Information 2026, 17(1), 100; https://doi.org/10.3390/info17010100 - 19 Jan 2026
Viewed by 414
Abstract
To address the issues of insufficient privacy protection, lack of confidentiality, and absence of traceability mechanisms in resource-constrained application scenarios such as IoT nodes or mobile network group communications, this paper proposes a traceable ring signcryption privacy protection scheme based on the SM9 [...] Read more.
To address the issues of insufficient privacy protection, lack of confidentiality, and absence of traceability mechanisms in resource-constrained application scenarios such as IoT nodes or mobile network group communications, this paper proposes a traceable ring signcryption privacy protection scheme based on the SM9 algorithm. In detail, the ring signcryption structure is designed based on the SM9 identity-based cryptography algorithm framework. Additionally, the scheme introduces a dynamic accumulator to compress ciphertext length and optimizes the algorithm to improve computational efficiency. Under the random oracle model, it is proved that the scheme has unforgeability, confidentiality, and conditional anonymity, and it is also demonstrated that conditional anonymity can be used to trace the identity of the actual signcryptor in the event of a dispute. Performance analysis shows that, compared with related schemes, this scheme improves the efficiency of signcryption, and the size of the signcryption ciphertext remains at a constant level. Full article
(This article belongs to the Special Issue Privacy-Preserving Data Analytics and Secure Computation)
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24 pages, 2429 KB  
Article
Secure Streaming Data Encryption and Query Scheme with Electric Vehicle Key Management
by Zhicheng Li, Jian Xu, Fan Wu, Cen Sun, Xiaomin Wu and Xiangliang Fang
Information 2026, 17(1), 18; https://doi.org/10.3390/info17010018 - 25 Dec 2025
Viewed by 569
Abstract
The rapid proliferation of Electric Vehicle (EV) infrastructures has led to the massive generation of high-frequency streaming data uploaded to cloud platforms for real-time analysis, while such data supports intelligent energy management and behavioral analytics, it also encapsulates sensitive user information, the disclosure [...] Read more.
The rapid proliferation of Electric Vehicle (EV) infrastructures has led to the massive generation of high-frequency streaming data uploaded to cloud platforms for real-time analysis, while such data supports intelligent energy management and behavioral analytics, it also encapsulates sensitive user information, the disclosure or misuse of which can lead to significant privacy and security threats. This work addresses these challenges by developing a secure and scalable scheme for protecting and verifying streaming data during storage and collaborative analysis. The proposed scheme ensures end-to-end confidentiality, forward security, and integrity verification while supporting efficient encrypted aggregation and fine-grained, time-based authorization. It introduces a lightweight mechanism that hierarchically organizes cryptographic keys and ciphertexts over time, enabling privacy-preserving queries without decrypting individual data points. Building on this foundation, an electric vehicle key management and query system is further designed to integrate the proposed encryption and verification scheme into practical V2X environments. The system supports privacy-preserving data sharing, verifiable statistical analytics, and flexible access control across heterogeneous cloud and edge infrastructures. Analytical and experimental evidence show that the designed system attains rigorous security guarantees alongside excellent efficiency and scalability, rendering it ideal for large-scale electric vehicle data protection and analysis tasks. Full article
(This article belongs to the Special Issue Privacy-Preserving Data Analytics and Secure Computation)
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15 pages, 3162 KB  
Article
OTSU-UCAN: An OTSU-Based Integrated Satellite–Terrestrial Information System for 6G in Vehicle Navigation
by Yawei Li, Kui Lu, Gang Cao, Shuyu Fan, Mingyue Zhang, Bohan Li and Tao Li
Information 2025, 16(12), 1072; https://doi.org/10.3390/info16121072 - 4 Dec 2025
Viewed by 490
Abstract
One of the bottlenecks hindering the applications (e.g., vehicle navigation) of blockchain–UCAN is privacy. A sharded blockchain can protect vehicle data to a certain extent. However, unbalanced shard loads lead to low throughput and poor feature extraction in blockchain–UCAN. This paper proposes an [...] Read more.
One of the bottlenecks hindering the applications (e.g., vehicle navigation) of blockchain–UCAN is privacy. A sharded blockchain can protect vehicle data to a certain extent. However, unbalanced shard loads lead to low throughput and poor feature extraction in blockchain–UCAN. This paper proposes an optimal image binarization method (OTSU-GK) to enhance image features and reduce the amount of uploaded data, thereby improving throughput. Specifically, OTSU-GK uses a Gaussian kernel method where the parameters are optimized using grid search to improve the calculation of the threshold. Additionally, we have designed a Node Load Score (NLS)-based sharding blockchain, which considers the number of historical transactions, transaction types, transaction frequency, and other metrics to balance the sharding loads and further improve throughput. The experimental results show that OTSU-GK improves by 74.3%, 58.7%, and 83% in SSIM, RMSE/MAE/AER, and throughput. In addition, it reduces IL by 70.3% compared to other methods. Full article
(This article belongs to the Special Issue Privacy-Preserving Data Analytics and Secure Computation)
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