A Study on the Data Pseudonymization Methodology for Defense Training Data as Artificial Intelligence Technology is applied to the Defense Field

Hyunsuk Cho, Sujin Kang, Dongrae Cho, Yeongseop Shin
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Abstract

Recently, in the defense field, efforts are being made to collect data by building data centers to incorporate artificial intelligence technology. Weapon system training data can be used as input data for artificial intelligence models and can be used as high-quality data to maximize training performance and develop military strategies. However, training data contains personal information such as the names and military numbers of the personnel who operated the equipment, and training records that reveal the characteristics of the weapon system. If such data is passed on to the enemy, not only the specifications and performance of the weapon system but also the proficiency of each operator may be exposed. In this paper, we propose a pseudonym processing methodology for education and training data security and also suggest a direction for revising related laws.
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人工智能技术应用于国防领域的国防训练数据假名化方法研究
最近,在国防领域,正在努力通过建立数据中心来整合人工智能技术来收集数据。武器系统训练数据可以作为人工智能模型的输入数据,可以作为高质量的数据来最大化训练性能和制定军事战略。然而,训练数据包含个人信息,如操作设备的人员的姓名和军号,以及揭示武器系统特征的训练记录。如果这些数据被传递给敌人,不仅武器系统的规格和性能,而且每个操作人员的熟练程度都可能暴露。本文提出了一种教育培训数据安全的假名处理方法,并提出了相关法律的修订方向。
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