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Research on the Application of Privacy-enhancing Technologies in AI-driven Automated Risk Detection Systems
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Abstract
With the widespread application of artificial intelligence in automated risk detection systems, the processing and analysis of large volumes of sensitive data face significant privacy risks. This study focuses on privacy-enhancing technologies, emphasizing the practical implementation of differential privacy, federated learning, homomorphic encryption, and secure multi-party computation within risk detection systems. Findings indicate that the appropriate use of these technologies can enhance model predictive capabilities and overall system reliability while ensuring data security. Additionally, the study examines potential challenges related to system performance, data heterogeneity, and model accuracy, proposing corresponding optimization strategies, providing technical guidance, and practical reference for improving the security and sustainable development of AI-driven risk detection systems.
Keywords
Artificial intelligence; detection systems; differential privacy; federated learning
References
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How to cite this paper
Research on the Application of Privacy-enhancing Technologies in AI-driven Automated Risk Detection Systems
How to cite this paper: Mingjie Chen. (2025) Research on the Application of Privacy-enhancing Technologies in AI-driven Automated Risk Detection Systems. Advances in Computer and Communication, 6(4), 173-177.
DOI: http://dx.doi.org/10.26855/acc.2025.10.003
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