智能城市数字孪生体设计中的隐私增强技术

Gabriela Ahmadi-Assalemi, Haider M. Al-Khateeb, Amar Aggoun
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引用次数: 0

摘要

数字孪生技术——由数据丰富的模型和机器学习组成——允许智慧城市应用的运营商获得复杂网络物理模型的准确表示。然而,必须通过将隐私保护机制集成到DT系统设计中,作为有效的深度防御策略的一部分,才能实现对弹性数据保护的隐性需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Privacy-enhancing technologies in the design of digital twins for smart cities
Digital twin technologies – comprised of data-rich models and machine learning – allow the operators of smart city applications to gain an accurate representation of complex cyber-physical models. However, the implicit need for resilient data protection must be achieved by integrating privacy-preserving mechanisms into the DT system design as part of an effective defence-in-depth strategy.
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