National Engineering Laboratory for Wireless Security, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
Westone Cryptologic Research Center, Beijing 100070, China.
Sensors (Basel). 2018 Aug 13;18(8):2659. doi: 10.3390/s18082659.
As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices' privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.
作为云计算的扩展，雾计算近年来受到了更多的关注。它可以解决云计算中存在的高延迟、缺乏对移动性和位置感知的支持等问题。在物联网（IoT）中，可以将一系列物联网设备连接到雾节点，这些雾节点辅助云服务中心提前存储和处理一部分数据。这不仅可以减轻数据处理的压力，还可以提高实时性和服务质量。然而，雾节点的数据处理存在许多挑战性问题，例如虚假数据注入攻击、数据篡改攻击和物联网设备的隐私侵犯。在本文中，我们基于 Paillier 同态加密方案，使用盲因子设计了雾计算中的隐私保护数据聚合方案。无论雾节点和云控制中心是否诚实，所提出的方案都能确保注入的数据来自合法的物联网设备，并且不会被修改和泄露。该方案还具有容错性，即使某些雾设备出现故障，也不会影响其他设备的数据采集。此外，安全性分析和性能评估表明，所提出的方案是安全有效的。
