Scholarlink

<p>The IoT Smart grid systems require privacy-sensitive, scalable, and secure communication, but the traditional cryptographic methods are susceptible to quantum attacks, central failure, and high computational costs. In this paper, the author presents a multi-layered structure that uses various yet complementary approaches in order to solve these issues. Quantum Key Distribution (QKD) is applied to create quantum-resilient key management to provide the secure transmission against future quantum attacks. Homomorphic Encryption (HE) allows the computations to be performed on encrypted data to preserve privacy and minimize the risks of exposure at some point during the processing. Federated Learning (FL) is used to assist the decentralized intelligence, where anomaly detection models are trained together without the need of transferring sensitive raw data. The combination of the two techniques produces a hybrid architecture that is both highly secure and scalable. Experimental validation on real-world smart grid data indicates the 42% communication overhead reduction versus traditional encryption, a 35% improvement in anomaly detection with FL and strong resistance to quantum attack and side-channel attack simulation. The findings support the fact the suggested solution provides end-to-end confidentiality, integrity, and performance efficiency, which will form a basis of quantum-resilience and privacy-preserving communication in future smart grid IoT network.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Secure and scalable smart grid IoT communication through quantum key distribution, homomorphic encryption, and federated learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月26日

查看全文
  • Prashant Kumar Shukla,
  • Suchi Mishra,
  • Sachin Tiwari,
  • Ankur Pandey,
  • Najah Kalifah Almazmomi

摘要

The IoT Smart grid systems require privacy-sensitive, scalable, and secure communication, but the traditional cryptographic methods are susceptible to quantum attacks, central failure, and high computational costs. In this paper, the author presents a multi-layered structure that uses various yet complementary approaches in order to solve these issues. Quantum Key Distribution (QKD) is applied to create quantum-resilient key management to provide the secure transmission against future quantum attacks. Homomorphic Encryption (HE) allows the computations to be performed on encrypted data to preserve privacy and minimize the risks of exposure at some point during the processing. Federated Learning (FL) is used to assist the decentralized intelligence, where anomaly detection models are trained together without the need of transferring sensitive raw data. The combination of the two techniques produces a hybrid architecture that is both highly secure and scalable. Experimental validation on real-world smart grid data indicates the 42% communication overhead reduction versus traditional encryption, a 35% improvement in anomaly detection with FL and strong resistance to quantum attack and side-channel attack simulation. The findings support the fact the suggested solution provides end-to-end confidentiality, integrity, and performance efficiency, which will form a basis of quantum-resilience and privacy-preserving communication in future smart grid IoT network.