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Future Internet
Special Issues
Security and Privacy in AI-Powered Systems
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Security and Privacy in AI-Powered Systems

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 1642

Special Issue Editors

Dr. Bo Liu

E-Mail Website
Guest Editor
School of Computer Science, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: cyber security; privacy; wireless communications networks; broadcasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tianqing Zhu

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Macau 999078, China
Interests: cybersecurity; privacy; AI

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is transforming industries by driving innovation in areas such as healthcare, finance, and smart cities. However, as AI-powered systems become integral to critical infrastructure and everyday life, they bring significant security and privacy challenges. These systems are vulnerable to adversarial attacks, data breaches, and the misuse of sensitive information, raising concerns about their trustworthiness and ethical use.

This Special Issue seeks to explore innovative solutions for enhancing the security and privacy of AI-powered systems. Topics of interest include security vulnerabilities, privacy-preserving AI techniques, adversarial machine learning defenses, secure deployment practices, and legal or ethical considerations. We aim to enhance interdisciplinary discussions that address both technical and societal dimensions, contributing to the development of robust, privacy-aware AI systems.

Key topics of interest include, but are not limited to, the following:

  • Security vulnerabilities in AI algorithms and models;
  • Privacy-preserving techniques in AI-powered data analytics;
  • Secure and trustworthy AI model deployment;
  • Adversarial machine learning and defenses;
  • Ethical and legal considerations in AI security and privacy;
  • Real-world case studies of securing AI-powered applications.

Dr. Bo Liu
Prof. Dr. Tianqing Zhu
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 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. Future Internet 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 1600 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

  • AI security
  • privacy-preserving AI
  • adversarial machine learning
  • secure AI deployment
  • ethical AI
  • federated learning
  • differential privacy
  • data protection
  • trustworthy AI systems

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Published Papers (2 papers)

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Research

24 pages, 1537 KiB  
Article
Privacy-Aware Hierarchical Federated Learning in Healthcare: Integrating Differential Privacy and Secure Multi-Party Computation
by Jatinder Pal Singh, Aqsa Aqsa, Imran Ghani, Raj Sonani and Vijay Govindarajan
Future Internet 2025, 17(8), 345; https://doi.org/10.3390/fi17080345 - 31 Jul 2025
Viewed by 440
Abstract
The development of big data analytics in healthcare has created a demand for privacy-conscious and scalable machine learning algorithms that can allow the use of patient information across different healthcare organizations. In this study, the difficulties that come with traditional federated learning frameworks [...] Read more.
The development of big data analytics in healthcare has created a demand for privacy-conscious and scalable machine learning algorithms that can allow the use of patient information across different healthcare organizations. In this study, the difficulties that come with traditional federated learning frameworks in healthcare sectors, such as scalability, computational effectiveness, and preserving patient privacy for numerous healthcare systems, are discussed. In this work, a new conceptual model known as Hierarchical Federated Learning (HFL) for large, integrated healthcare organizations that include several institutions is proposed. The first level of aggregation forms regional centers where local updates are first collected and then sent to the second level of aggregation to form the global update, thus reducing the message-passing traffic and improving the scalability of the HFL architecture. Furthermore, the HFL framework leveraged more robust privacy characteristics such as Local Differential Privacy (LDP), Gaussian Differential Privacy (GDP), Secure Multi-Party Computation (SMPC) and Homomorphic Encryption (HE). In addition, a Novel Aggregated Gradient Perturbation Mechanism is presented to alleviate noise in model updates and maintain privacy and utility. The performance of the proposed HFL framework is evaluated on real-life healthcare datasets and an artificial dataset created using Generative Adversarial Networks (GANs), showing that the proposed HFL framework is better than other methods. Our approach provided an accuracy of around 97% and 30% less privacy leakage compared to the existing models of FLBM-IoT and PPFLB. The proposed HFL approach can help to find the optimal balance between privacy and model performance, which is crucial for healthcare applications and scalable and secure solutions. Full article
(This article belongs to the Special Issue Security and Privacy in AI-Powered Systems)
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22 pages, 698 KiB  
Article
An AI-Driven Framework for Integrated Security and Privacy in Internet of Things Using Quantum-Resistant Blockchain
by Mahmoud Elkhodr
Future Internet 2025, 17(6), 246; https://doi.org/10.3390/fi17060246 - 30 May 2025
Viewed by 895
Abstract
The growing deployment of the Internet of Things (IoT) across various sectors introduces significant security and privacy challenges. Although numerous individual solutions exist, comprehensive frameworks that effectively combine advanced technologies to address evolving threats are lacking. This paper presents the Integrated Adaptive Security [...] Read more.
The growing deployment of the Internet of Things (IoT) across various sectors introduces significant security and privacy challenges. Although numerous individual solutions exist, comprehensive frameworks that effectively combine advanced technologies to address evolving threats are lacking. This paper presents the Integrated Adaptive Security Framework for IoT (IASF-IoT), which integrates artificial intelligence, blockchain technology, and quantum-resistant cryptography into a unified solution tailored for IoT environments. Central to the framework is an adaptive AI-driven security orchestration mechanism, complemented by blockchain-based identity management, lightweight quantum-resistant protocols, and Digital Twins to predict and proactively mitigate threats. A theoretical performance model and large-scale simulation involving 1000 heterogeneous IoT devices were used to evaluate the framework. Results showed that IASF-IoT achieved detection accuracy between 85% and 99%, with simulated energy consumption remaining below 1.5 mAh per day and response times averaging around 2 s. These findings suggest that the framework offers strong potential for scalable, low-overhead security in resource-constrained IoT environments. Full article
(This article belongs to the Special Issue Security and Privacy in AI-Powered Systems)
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