Security Challenges and Opportunities of Artificial Intelligence/Big Data Scenarios

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Bol This Reprint presents a carefully curated collection of peer-reviewed, open access articles from the Electronics Special Issue "Security Challenges and Opportunities of Artificial Intelligence/Big Data Scenarios", capturing state-of-the-art research at the intersection of artificial intelligence, big data analytics, cybersecurity, privacy engineering, and trustworthy intelligent systems.The contributions in this volume address a broad spectrum of critical topics, including adversarial machine learning, data leakage and privacy preservation, secure model training and deployment, authentication and access control in AI-driven systems, ethical risks and bias mitigation, regulatory compliance for big data platforms, and resilience against cyber threats in cloud, edge, and IoT environments. Beyond technical defenses and vulnerability detection, this Reprint highlights the dual nature of AI and big data: while these technologies introduce novel attack surfaces and systemic security risks, they also empower advanced threat intelligence, automated anomaly detection, predictive security analytics, and robust risk management across public service scenarios.We express our sincere gratitude to all contributing authors for their impactful work, to the reviewers for their rigorous and constructive feedback, and to the editorial and publishing teams at MDPI for their dedicated support throughout the process.

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This Reprint presents a carefully curated collection of peer-reviewed, open access articles from the Electronics Special Issue "Security Challenges and Opportunities of Artificial Intelligence/Big Data Scenarios", capturing state-of-the-art research at the intersection of artificial intelligence, big data analytics, cybersecurity, privacy engineering, and trustworthy intelligent systems.The contributions in this volume address a broad spectrum of critical topics, including adversarial machine learning, data leakage and privacy preservation, secure model training and deployment, authentication and access control in AI-driven systems, ethical risks and bias mitigation, regulatory compliance for big data platforms, and resilience against cyber threats in cloud, edge, and IoT environments. Beyond technical defenses and vulnerability detection, this Reprint highlights the dual nature of AI and big data: while these technologies introduce novel attack surfaces and systemic security risks, they also empower advanced threat intelligence, automated anomaly detection, predictive security analytics, and robust risk management across public service scenarios.We express our sincere gratitude to all contributing authors for their impactful work, to the reviewers for their rigorous and constructive feedback, and to the editorial and publishing teams at MDPI for their dedicated support throughout the process.

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Pagina's: 190, Hardcover, MDPI AG


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  • 9783725873807
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