**Fully Homomorphic Encryption over Encrypted Data using 3 Machine Learning Algorithms**

It focuses on exploring privacy-preserving machine learning models, such as decision tree regression, linear regression, and support vector machines, in the context of homomorphic encryption. The main goal is to improve understanding of the complex link between data privacy and model accuracy. The study's goal with this inquiry is to contribute to the advancement of theoretical understanding and practical consequences in the field of privacy-preserving machine learning. The research questions focus on the effectiveness of homomorphic encryption in protecting sensitive data during model training and inference processes, whereas the objectives aim to assess the impact of privacy-preserving techniques on the overall performance and accuracy of the chosen machine learning models. By emphasizing the advantages and limits of different models in a privacy-preserving setting, this study provides the framework for the establishment of a privacy-aware ecosystem in machine learning, addressing a critical issue in the area. In essence, this study aims to advance knowledge in the rapidly evolving field of privacy-preserving machine learning by addressing specific research questions and objectives, anticipating a more nuanced understanding that bridges the gap between data privacy and model accuracy, and paving the way for informed decisions and advances in both theory and practice.

Summary of research findings: The literature review most likely revealed a paucity of detailed studies on the practical consequences of privacy-preserving machine learning models in the context of homomorphic encryption. This study not only investigates these models, but also evaluates their performance measures in the encrypted domain, such as precision scores and R^2 score. This empirical review adds essential insights that were likely absent in the previous literature, filling the gap by presenting practical findings on the applicability and limitations of privacy-preserving machine learning models using homomorphic encryption. It gives a thorough analysis of the performance of privacy-preserving machine learning models in the context of homomorphic encryption. The decision tree model stood out as a top performer, demonstrating both flexibility and accuracy in the encrypted domain. Notably, it obtained a precision score of 0.97, indicating its ability to make precise predictions. Furthermore, the decision tree model achieved a near approximation to unambiguous predictions, demonstrating its strong capabilities.The support vector machine also provided an important contribution, demonstrating high accuracy in the homomorphically encrypted environment. This makes the support vector machine an attractive choice for safe machine learning applications where maintaining data privacy is critical. In essence, the study's findings highlight the efficacy of these privacy-preserving models in achieving high levels of accuracy while operating within the constraints of homomorphic encryption, opening up promising avenues for the integration of secure machine learning practices in sensitive data environments.