## 2 Literature Review  
Privacy and security are essential when dealing with smart home or IoT sensor datasets, especially in the context of ML training [9; 10]. By performing computations on encrypted data, one can create a robust encryption solution, thereby maintaining privacy and security. Thus, there is an extensive and diverse literature regarding HE and its applications in privacy-preserving data processing. This review summarizes the important studies conducted on the use and implementation of HE in various domains. The HE model offers a promising solution by providing computations on encrypted data without the need to decrypt it first, thus maintaining data confidentiality throughout the process [11]. However, the SHE and PHE incur big efficiency and scalability drawbacks and may be prohibitive in large-scale applications such as smart home environments. To overcome these drawbacks, the possibility of using hybrid HE has been suggested. A combination of symmetric cryptography and HE achieves the same goals as HE but with increased performance and scalability [12]. The author proposed a Privacy-Preserving Machine Learning (PPML) scheme tailored for end devices, demonstrating minimal communication and computation costs while maintaining data privacy. Another model [13], a hybrid HE-based PPML, was developed for the classification of heart disease with sensitive ECG data, showing only a slight accuracy reduction compared to plaintext. FHE was used to secure the computations and predictions in machine learning tasks such as predicting diabetes using logistic regression over encrypted datasets, which proved that during this process, data remained private and secure. Specifically, homomorphic evaluation of machine learning algorithms, deep Convolutional Neural Networks (CNNs), and Weightless Neural Networks (WNNs), has proven to be fairly practical in both inference and training on encrypted data, wherein high speedups have been achieved with competitive accuracy. Similarly, HETAL [14] is an efficient HE-based transfer learning algorithm that focuses on protecting client privacy during training tasks by encrypting client data using the CKKS HE scheme. The study introduces an efficient encrypted matrix multiplication algorithm that is significantly faster, as well as a highly precise softmax approximation algorithm. Thus, the integration of Federated Learning (FL) with HE in the context of smart home sensor datasets provides a robust solution for privacy and security [10]. By utilizing AI models and the CKKS algorithm, FL enables collaborative training without sharing raw data, while HE ensures secure computation on encrypted data. This approach guarantees privacy preservation and accurate results, as demonstrated by achieving the highest average accuracy. In ref. [15], the Multi-Key HE Logistic Regression (MK-HELR) algorithm is introduced. It lets you use shared datasets to do logistic regression on encrypted data from multiple parties while keeping the data private. In Ref. [16], FHE has been applied even to more complex models in machine learning, like CNNs, and has seen huge improvements in efficiency and accuracy. When comparing convolutions, optimized FHE methods cut the time needed by up to 46%. This implies that deep learning tasks such as CIFAR  
Figure 1: Smart home data with homomorphic encryption and machine learning depiction. 10/100 and ImageNet can utilize FHE. Better models, such as the ResNet-20 model with RNS-CKKS FHE [17], can produce results that are very similar to those of models that are not encrypted. For example, the ResNet-20 model with RNS-CKKS FHE obtained a score of 92.43% for CIFAR-10. This opens up new ways to use FHE with deep learning models in smart home environments. Advances in HE have huge potential for securing smart home sensor datasets while enabling sophisticated machine learning applications. In Table 1, one can see a detailed overview of all approaches, with additional methods included for a more comprehensive comparison.