The Challenges of Data-Driven Systems: Intelligence, Security and Trust
A special issue of Future Internet (ISSN 1999-5903).
Deadline for manuscript submissions: 28 February 2026 | Viewed by 58
Special Issue Editors
Interests: software engineering; cross-border data processing; system modeling
Special Issues, Collections and Topics in MDPI journals
Interests: earth observation; satellite image analysis
Interests: data analysis; data-driven; explainable modelling; artificial intelligence and applications
Special Issues, Collections and Topics in MDPI journals
Interests: data and knowledge engineering; artificial intelligence and applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Data-driven systems (DDSs) are transforming industries through the use of artificial intelligence (AI) and machine learning (ML), yet they face critical challenges when it comes to intelligence, security, and trust. These intertwined issues require multidisciplinary research to ensure the sustainable development of society.
Modern AI models prioritize predictive accuracy, but their complexity often compromises their security. Even advanced techniques like federated learning, which is designed to enhance privacy, reveal vulnerabilities when malicious actors exploit model updates in collaborative environments. The pursuit of intelligence thus amplifies attack surfaces, necessitating the use of robust defenses such as formal verification and adversarial training.
Security measures intended to protect systems can inadvertently harm trust. For example, differential privacy techniques add noise to datasets to preserve anonymity, but excessive noise reduces data utility, leading users to question the system’s reliability. Conversely, excessive emphasis on transparency risks exposing a system’s vulnerabilities to attackers. Blockchain-based auditing and decentralized trust anchors aim to resolve this paradox by enabling verifiable transparency without compromising security. However, these solutions require significant computational overhead, limiting their scalability to real-time applications like IoT networks.
Trust remains essential for DDS adoption, yet opaque AI decision-making undermines user confidence. Explainable AI (XAI) methods are gaining traction, offering interpretable insights into model behavior. However, balancing transparency with performance remains challenging; simplified explanations often oversimplify complex models, while granular details overwhelm non-expert users.
The trilemma of intelligence, security and trust in data-driven systems reflects a broader tension between innovation and accountability. Addressing it requires not only algorithmic breakthroughs but also collaborative governance, transparent user engagement, and continuous adaptation to evolving threats. By prioritizing interdisciplinary approaches—from secure-by-design AI to participatory policymaking—we can foster systems that are not only smart but also resilient and trustworthy.
All related theoretical and applied works will be considered for publication in this Special Issue. We look forward to receiving your contributions.
Prof. Dr. Zhiming Cai
Prof. Dr. Yuqi Bai
Prof. Dr. Wencai Du
Prof. Dr. Zhihai Wang
Guest Editors
Manuscript Submission Information
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Keywords
- data-driven
- intelligence
- security
- trust
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