Evaluating the Efficacy of Homomorphic Encryption and Secure Multiparty Computation for Preserving Confidentiality in Decentralized Data Ecosystems

Abstract

In decentralized data ecosystems, the confidentiality of sensitive information remains a significant challenge, particularly in the face of collaborative data analysis. This paper evaluates two cryptographic paradigms—Homomorphic Encryption (HE) and Secure Multiparty Computation (SMC)—for their potential to ensure data privacy without compromising utility. Using comparative metrics such as computational overhead, scalability, and confidentiality guarantees, we conduct an evaluative study rooted in cryptographic theory and practical implementations. Our findings reveal key trade-offs in applying HE and SMC within decentralized frameworks such as federated learning and blockchain-based networks. We conclude by identifying deployment scenarios best suited to each paradigm and highlighting future research opportunities in hybridized models.