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 2025 | Viewed by 384

Special Issue Editors

School of Computer Science, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: cyber security; privacy; wireless communications networks; broadcasting
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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

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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 (1 paper)

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Research

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 182
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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