Generative AI for Data Privacy and Anomaly Detection
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 88
Special Issue Editor
Special Issue Information
Dear Colleagues,
Recent advances in generative artificial intelligence (AI) have opened new opportunities for addressing critical challenges in data privacy and anomaly detection. Generative models such as variational autoencoders, generative adversarial networks, diffusion models, and large language models provide powerful tools for synthesizing realistic yet privacy-preserving data, enabling secure data sharing and collaborative analysis across domains. At the same time, these models offer innovative mechanisms for identifying anomalous patterns by leveraging learned representations of normality.
This Special Issue aims to gather cutting-edge research that bridges generative AI, privacy-preserving techniques, and anomaly detection. We invite contributions that present novel algorithms, theoretical insights, or applied systems that enhance data security, protect sensitive information, and improve the reliability of anomaly detection in diverse fields, including healthcare, finance, manufacturing, cybersecurity, and social sciences. Both methodological papers and application-driven studies are welcome, with a particular emphasis on interdisciplinary approaches that advance the state of the art in trustworthy AI.
Dr. Dongha Kim
Guest Editor
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Keywords
- generative AI
- data privacy
- privacy-preserving
- data synthesis
- anomaly detection
- trustworthy AI
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