In the digital economy, individual data sovereignty is a requirement for sustainable and secure development. With an increase in the number of outsourced computations, due to the adoption of web services, artificial intelligence and research-based innovation, privacy became one of the main concerns for individuals and third parties alike. Fully Homomorphic Encryption (FHE) addresses this gap by enabling secure computations on encrypted data. This paper reviews and presents the FHE literature in a structural manner, covering the topics of mathematical constructions, security, efficiency and functionality. In doing so, it highlights the current state-of-the-art techniques and provides a systemic comparison with other privacy enhancing mechanisms. In addition, it discusses its applicability in the practical domain, such as Confidential Machine Learning, Medical Data Analysis and Recommender Systems, mentioning potential impediments and their solutions for mass adoption.