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Applied Sciences
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Progress in Information Security and Privacy
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Progress in Information Security and Privacy

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1303

Special Issue Editor

Prof. Dr. Hiroaki Kikuchi

E-Mail Website
Guest Editor
Department of Frontier Media Science, School of Interdisciplinary Mathematical Sciences, Meiji University, Tokyo 164-8525, Japan
Interests: data privacy; network security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid growth of data-driven technologies for large-scale data processing has raised significant concerns about data privacy and security. This Special Issue aims to explore various challenges related to big data and address issues regarding the advanced technologies. The scope of this Special Issue includes differential privacy, anonymization schemes for multi-dimensional data, study on various threat models, and human aspects regarding privacy policies for data-driven technologies.

We look forward to your submissions.

Prof. Dr. Hiroaki Kikuchi
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • differential privacy
  • data anonymization
  • privacy policy
  • membership inference
  • attribute inference
  • serveylance camera

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Published Papers (2 papers)

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Research

35 pages, 1511 KB  
Article
Curriculum Learning and Pattern-Aware Highly Efficient Privacy-Preserving Scheme for Mixed Data Outsourcing with Minimal Utility Loss
by Abdul Majeed, Kyunghyun Lee and Seong Oun Hwang
Appl. Sci. 2025, 15(21), 11849; https://doi.org/10.3390/app152111849 - 6 Nov 2025
Viewed by 323
Abstract
A complex problem when outsourcing personal data for public use is balancing privacy protection with utility, and anonymization is a viable solution to address this issue. However, conventional anonymization methods often overlook global information regarding the composition of attributes in data, leading to [...] Read more.
A complex problem when outsourcing personal data for public use is balancing privacy protection with utility, and anonymization is a viable solution to address this issue. However, conventional anonymization methods often overlook global information regarding the composition of attributes in data, leading to unnecessary computations and high utility loss. To address these problems, we propose a curriculum learning (CL)-based, pattern-aware privacy-preserving scheme that exploits information about attribute composition in the data to enhance utility and privacy without performing unnecessary computations. The CL approach significantly reduces time overheads by sorting data by complexity, and only the most complex (e.g., privacy-sensitive) parts of the data are processed. Our scheme considers both diversity and similarity when forming clusters to effectively address the privacy–utility trade-off. Our scheme prevents substantial changes in data during generalization by protecting generic portions of the data from futile anonymization, and only a limited amount of data is anonymized through a joint application of differential privacy and k-anonymity. We attain promising results by rigorously testing the proposed scheme on three benchmark datasets. Compared to recent anonymization methods, our scheme reduces time complexity by 74.33%, improves data utility by 19.67% and 68.33% across two evaluation metrics, and enhances privacy protection by 29.19%. Our scheme performs 82.66% fewer lookups in generalization hierarchies than existing anonymization methods. In addition, our scheme is very lightweight and is 1.95× faster than the parallel implementation architectures. Our scheme can effectively solve the trade-off between privacy and utility better than prior works in outsourcing personal data enclosed in tabular form. Full article
(This article belongs to the Special Issue Progress in Information Security and Privacy)
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41 pages, 2272 KB  
Article
Bridging Computational Structures with Philosophical Categories in Sophimatics and Data Protection Policy with AI Reasoning
by Gerardo Iovane and Giovanni Iovane
Appl. Sci. 2025, 15(20), 10879; https://doi.org/10.3390/app152010879 - 10 Oct 2025
Cited by 1 | Viewed by 519
Abstract
Contemporary artificial intelligence excels at pattern recognition but lacks genuine understanding, temporal awareness, and ethical reasoning. Critics argue that AI systems manipulate statistical correlations without grasping concepts, time, or moral implications. This article presents Phase 2, a component of the emerging infrastructure called [...] Read more.
Contemporary artificial intelligence excels at pattern recognition but lacks genuine understanding, temporal awareness, and ethical reasoning. Critics argue that AI systems manipulate statistical correlations without grasping concepts, time, or moral implications. This article presents Phase 2, a component of the emerging infrastructure called Sophimatics, a computational framework that translates philosophical categories into working algorithms through the integration of complex time. Our approach operationalizes Aristotelian substance theory, Augustinian temporal consciousness, Husserlian intentionality, and Hegelian dialectics within a unified temporal–semantic architecture. The system represents time as both chronological and experiential, allowing navigation between memory and imagination while maintaining conceptual coherence. Validation through a Data Protection Policy use case demonstrates significant improvements: confidence in decisions increased from 6.50 to 9.40 on a decimal scale, temporal awareness from 2.00 to 9.50, and regulatory compliance from 6.00 to 9.00 compared to traditional approaches. The framework successfully links philosophical authenticity with computational practicality, offering greater ethical consistency and contextual adaptability for AI systems that require temporal reasoning and ethical foundations. Full article
(This article belongs to the Special Issue Progress in Information Security and Privacy)
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