The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of connected devices raises critical concerns regarding security and privacy of the user data. In this paper, we propose a differential privacy-based system to ensure comprehensive security for data generated by smart homes. We employ the randomized response technique for the data and utilize Local Differential Privacy (LDP) to achieve data privacy. The data is then transmitted to an aggregator, where an obfuscation method is applied to ensure individual anonymity. Furthermore, we implement the Hidden Markov Model (HMM) technique at the aggregator level and apply differential privacy to the private data received from smart homes. Consequently, our approach achieves a dual layer of privacy protection, addressing the security concerns associated with IoT devices in smart cities.

翻译：物联网（IoT）设备在智能家居中的快速普及显著提升了生活质量，带来了更高的便利性、自动化水平及能源效率。然而，这种互联设备的激增引发了关于用户数据安全与隐私的严峻问题。本文提出一种基于差分隐私的系统，以确保智能家居生成数据的全面安全性。我们采用随机响应技术处理数据，并利用本地差分隐私（Local Differential Privacy, LDP）实现数据隐私保护。随后，数据被传输至聚合器，在该处应用混淆方法以确保个体匿名性。此外，我们在聚合器层级实现隐马尔可夫模型（Hidden Markov Model, HMM）技术，并对来自智能家居的私有数据施加差分隐私。因此，本文方法实现了双层隐私保护，有效应对智慧城市中物联网设备相关的安全关切。