What are the financial costs associated with the use of Homomorphic Encryption for privacy-preserving data analytics?

Abstract

Homomorphic Encryption (HE) enables computation directly on encrypted data, while offering strong cryptographic and privacy guarantees for data-driven sectors like healthcare, finance, and cloud computing. However, practical adoption of HE is severely limited by its computational overhead and specialized expertise requirements. This thesis investigates the financial feasibility of HE by analyzing its Total Cost of Ownership (TCO), an overlooked but crucial factor for researchers, companies, and cloud service customers deciding whether to adopt HE. Building on performance benchmarks from Guillot et al. and Lo et al., we evaluate the runtime, resource requirements, and personnel costs of Fully Homomorphic Encryption (FHE) across different workloads, and Amazon Web Service (AWS) cloud configurations. Our results show that encryption and expert labor dominate the cost, with encryption accounting for over 80% of the runtime and personnel expertise exceeding 98% of the total annual cost ($125,558.066). These findings highlight that, despite the security benefits of HE, its financial overhead restricts its adoption to organizations with substantial cryptographic expertise and budgets. This thesis equips researchers and companies with data-driven insight into HE's true economic impact and underlines the need for further optimization and automation to make HE accessible and cost-effective for broader, real-world use.