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Advances in Technologies for Data Privacy and Security
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Advances in Technologies for Data Privacy and Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 2162

Special Issue Editors

Dr. Tanja Pavleska

E-Mail Website
Guest Editor
Laboratory for Open Systems and Networks, Jozef Stefan Institute, Jamova Cesta 39, 1000 Ljubljana, Slovenia
Interests: information technology infrastructure; information assets; information security; interoperability
Dr. Reiner Creutzburg

E-Mail Website
Guest Editor
1. Technische Hochschule Brandenburg, Department of Informatics and Media, Magdeburger Str. 50, D-14770 Brandenburg, Germany
2. School of Technology and Architecture, Campus Berlin, SRH University of Applied Sciences Heidelberg, Sonnenallee 221c, D-15087 Berlin, Germany
Interests: cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era of ubiquitous data generation and rapidly advancing digital ecosystems, ensuring data privacy and security is a critical concern. The rise in artificial intelligence (AI)—particularly generative AI models—has amplified both the opportunities and risks in this domain. While AI offers powerful tools for detecting threats, automating security protocols, and enhancing privacy-preserving computation, it also introduces novel vulnerabilities, such as model inversion attacks, data leakage, and the misuse of synthetic data.

This Special Issue of Applied Sciences explores cutting-edge developments at the intersection of data privacy, security, and intelligent systems. It brings together theoretical innovations and practical applications spanning cryptographic frameworks, secure and federated learning, differential privacy, homomorphic encryption, and blockchain-based approaches. Special attention is given to privacy risks and mitigation strategies in AI systems, including techniques to secure training data, interpret model behavior, and control the dissemination of generative content.

By highlighting these multidisciplinary advances, this issue aims to foster a comprehensive understanding of how to build secure, transparent, and trustworthy AI-driven technologies. It serves as a valuable resource for researchers, developers, and policymakers navigating the evolving challenges of safeguarding data in an increasingly AI-powered world.

Dr. Tanja Pavleska
Dr. Reiner Creutzburg
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. Applied Sciences 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 privacy
  • cybersecurity
  • artificial intelligence
  • generative AI
  • privacy -preserving machine learning
  • federated learning
  • differential privacy
  • model security
  • homomorphic encryption
  • blockchain security

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Published Papers (2 papers)

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Research

25 pages, 1408 KB  
Article
Addressing Memorization and Aggregation Risks in AI: A Knowledge Graph Approach to Privacy
by Jinhui Zuo and Seok-Won Lee
Appl. Sci. 2026, 16(4), 1796; https://doi.org/10.3390/app16041796 - 11 Feb 2026
Viewed by 607
Abstract
Recent studies have shown that AI models can memorize specific data records, resulting in sensitive data exposure through model access. Current privacy-enhancing technologies often overlook the crucial, context-dependent nature of privacy risk as they largely fail to account for the inherent relationships and [...] Read more.
Recent studies have shown that AI models can memorize specific data records, resulting in sensitive data exposure through model access. Current privacy-enhancing technologies often overlook the crucial, context-dependent nature of privacy risk as they largely fail to account for the inherent relationships and complex interactions between data records, leading to high risks associated with memorization and potential data aggregation. Our research first investigates two key factors influencing AI privacy risks: implicit connections and data redundancy. These experiments have shown that AI models learn subtle links between private data, even when they are discretely distributed. To address the privacy issue, we introduce PrivGraph, a hierarchically structured knowledge graph for modeling and aggregating private information. Based on PrivGraph, we introduce the Sensitivity Level Factor (SLF) to quantify the degree to which an individual’s private information is embedded in the data. In addition, we propose a PrivGraph-based knowledge probing method to facilitate post-training privacy assessments. Our experiments demonstrated that PrivGraph achieves comparable performance to existing models in the Personally Identifiable Information (PII) detection task, while effectively modeling the aggregation of private information even with lengthy texts and data obtained from multiple origins. Finally, we discuss PrivGraph’s integration into the AI engineering lifecycle for full-spectrum, full-lifecycle, and traceable privacy protection. Full article
(This article belongs to the Special Issue Advances in Technologies for Data Privacy and Security)
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31 pages, 2412 KB  
Article
Privacy-Preserving User Profiling Using MLP-Based Data Generalization
by Dardan Maraj, Renato Šoić, Antonia Žaja and Marin Vuković
Appl. Sci. 2026, 16(2), 848; https://doi.org/10.3390/app16020848 - 14 Jan 2026
Viewed by 568
Abstract
The rapid growth in Internet-based services has increased the demand for user data to enable personalized and adaptive digital experiences. These services typically require users to disclose various types of personal information, which are organized into user profiles and used to tailor content, [...] Read more.
The rapid growth in Internet-based services has increased the demand for user data to enable personalized and adaptive digital experiences. These services typically require users to disclose various types of personal information, which are organized into user profiles and used to tailor content, recommendations, and accessibility settings. However, achieving an effective balance between personalization accuracy and user data protection remains a persistent and complex challenge. Excessive data disclosure raises the risk of re-identification and privacy breaches, while excessive anonymization can significantly diminish personalization and overall service quality. In this paper, we address this trade-off by proposing a context-aware learning-based data generalization framework that preserves user privacy while maintaining the functional usefulness of personal data. We first conduct a systematic classification of user data commonly collected into five main categories: demographic, location, accessibility, preference, and behavior data. To generalize these data categories dynamically and adaptively, we use a Multi-Layer Perceptron (MLP) model that learns patterns across heterogeneous data types. Unlike traditional rule-based generalization techniques, the MLP-based approach captures nonlinear relationships, adapts to heterogeneous data distributions, and scales efficiently with large datasets. The proposed MLP-based generalization method reduces the granularity of personal data, preserving privacy without significantly compromising information usefulness. Experimental results show that the proposed method reduces the risk of re-identification to approximately 35%, compared to non-anonymized data, where the re-identification risk is about 80–90%. These findings highlight the potential of learning-based data generalization as a strategy for privacy-preserving personalization in modern Internet services. They also show how the proposed generalization method can be applied in practice to transform user data while maintaining both utility and confidentiality. Full article
(This article belongs to the Special Issue Advances in Technologies for Data Privacy and Security)
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