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：随着物联网（IoT）设备在智能家居中的快速扩展，极大地改善了生活质量，提供了更高的便利性、自动化和能源效率。然而，这些连接设备的大量增加引发了关于用户数据安全和隐私的重大担忧。在本文中，我们提出了一种基于差分隐私的系统，以确保智能家居生成的数据得到全面的安全保护。我们采用了随机响应技术用于数据，并利用本地差分隐私（LDP）实现数据隐私。然后将数据传输到聚合器，在其中应用模糊化方法，以确保个人匿名。此外，我们在聚合器级别实现了隐马尔科夫模型（HMM）技术，并对从智能家居接收的私有数据应用了差分隐私。因此，我们的方法实现了数据的两层隐私保护，解决了物联网在智能城市中存在的安全问题。