## 1 Introduction  
The advancement of IoT technology has significantly transformed modern life, which makes it an essential part of daily routines across various sectors. Technologies such as edge device-based artificial intelligence (AI) are increasingly extensive, especially in personal gadgets and smart home environments. This generation of smart homes, equipped with devices like smart meters, fitness trackers, and controllers for interconnected houses and smart cities, is reshaping how we interact with our surroundings and advancing modern society [1]. In a smart home environment, conventional home equipment can be remotely connected and operated through these technologies. These devices and sensors collect extensive amounts of personal data, which, when integrated into various platforms, use analytics to analyze human behavior, simplify decision-making, and offer personalized recommendations [2]. For example, it includes motion-activated lighting, thermostats that adjust room temperature based on real-time occupancy, activity recognition systems for elderly care, and intelligent security systems that identify family members, promoting ease, security, and efficiency within the household. However, while these advancements provide significant benefits, the sensitive nature of the data being collected poses serious privacy and security concerns. This study aims to address these concerns by comparing the performance of machine learning models with and without homomorphic encryption in a smart home environment. By doing so, we aim to explore the trade-offs between security and computational efficiency, filling the gap in existing research that has not sufficiently focused on secure yet efficient data handling in smart homes. Similarly, as AI replaces humans, IoT and smart technologies are taking advantage of the smart generation [3]. Instead of being constrained to basic data transmitters, devices are now intelligent robots that can understand data, make intelligent decisions, and adjust their performance according to user conditions [4; 5]. These advancements in technology and innovations provide significant benefits but also introduce new challenges, primarily regarding the _privacy and security_ of the generated data. Each device in a smart environment acts as a data source, continuously generating and collecting information about the resident's identification, habits, and preferences. These data, while valuable for decisive analysis, can reveal personal details about an individual's daily life routines and even health status [2; 6; 7]. The fear of being findable is still real, and it is dual. Unauthorized access to sensitive data generated in smart environments and excessive use of collected data for analysis compromise users' privacy and comfort levels. The breach of privacy in the first case was due to insecure network devices used in smart environments and a breach of privacy by third parties while using them for analysis. As a result, there should be a mechanism to secure smart environment datasets against unauthorized access and ensure their confidentiality while being shared for data mining. To this end, considering previously discussed privacy and security breaches, the scope of this research study is to investigate the implementation of HE to improve privacy in smart homes, with a focus on residents' data being used for analysis while remaining secure and private, as illustrated in Figure 1. The analysis includes anomaly detection, activity recognition, and voice assistance. In addition, the presented use case study results demonstrate the privacy of sensor-based datasets from the smart home using different ML models. From the practical implementation point of view, we used two well-known libraries, TFHE [8] and concrete-ml based on HE, to calculate the accuracy and execution time for different ML models. Using FHE with ML in smart homes, this research aims to strike a balance between the growing demand for smart home datasets and the critical need for the preservation of privacy.