# Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL
## 1 Introduction  
In today's digital age, the exponential growth of data generated by the Internet of Things (IoT) and other digital platforms has led to the emergence of vast repositories of information known as data lakes [1; 2]. These expansive, fluid collections of raw data, stored in their native format, offer unprecedented opportunities for analytics, innovation, and personalized services. The concept of data lakes has revolutionized the way businesses and organizations harness the power of big data, enabling the aggregation and analysis of varied data types to glean insights, drive decision making, and foster technological advancements [3; 4]. By integrating diverse datasets, data lakes facilitate comprehensive analytics that can predict trends, enhance efficiency, and create more tailored user experiences. However, the benefits of leveraging extensive datasets come with significant privacy and security challenges. As we navigate through this wealth of information, the line between valuable insights and invasive privacy breaches becomes increasingly blurred. The need to protect sensitive information while exploiting data to their full potential has never been more critical. Traditional cybersecurity measures often fall short of addressing the unique vulnerabilities presented by the interconnected and open nature of data lakes and IoT ecosystems. The dynamic landscape of digital threats necessitates innovative approaches to data security, ones that can an adeptly safeguard privacy without sacrificing the utility and benefits of these technological advancements [5; 6; 7]. The introduction of fully homomorphic encryption (FHE) stands out as an opportunity for addressing the privacy-security challenge [8; 9]. FHE is a form of encryption that allows computations to be performed on encrypted data, generating encrypted results that, once decrypted, reveal the outcome of operations as if they had been conducted on the original plaintext. This groundbreaking capability ensures that sensitive data can remain encryptedthroughout the analytical process, thereby preserving confidentiality while still enabling valuable insights to be extracted. The incorporation of FHE into the realms of data lakes and IoT represents a transformative approach to managing the inherent privacy and security risks associated with big data analytics. By enabling data to be processed in their encrypted form, FHE mitigates the risks of data breaches and unauthorized access, ensuring that the integrity and confidentiality of sensitive information are maintained. This is particularly pertinent in the context of data lakes, where the sheer volume and variety of data heighten the potential for privacy infringements. Nevertheless, it is important to note that homomorphic encryption does not provide a means to bypass privacy regulations such as the European General Data Protection Regulation (GDPR) [10]. For instance, it is not feasible for a cloud service to apply homomorphic encryption in order to derive insights from customer data while they remain encrypted. Rather, the outcomes of computations performed on encrypted data stay encrypted and can only be decrypted by the data's owner, such as a customer of a cloud service. This adds another limitation, which is that in situations where several distinct private data owners intend to partake in joint computational efforts, homomorphic encryption is likely not a viable option. This is because homomorphic encryption frameworks operate with a singular secret key, which is possessed by the owner of the data. In this paper, we offer a detailed empirical analysis of FHE using the Microsoft SEAL library, with the aim of understanding FHE's practical performance and scalability. We particularly shed light on the optimization challenges and potential solutions for implementing FHE in edge computing environments, such as those encountered with IoT devices, by comparing its performance on a traditional computing system (PC) and an edge device (NVIDIA Jetson Nano).