Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption

ArXi:2606.05129v1 Announce Type: cross Preserving data privacy is an important topic in structural data management and data mining. However, the issue of privacy leakage in distributed causal structure learning is a persistent challenge, especially in cases where data transmission and computation are required. In this paper, we propose a method based on fully homomorphic encryption (FHE) that performs calculations on ciphertexts, keeping data encrypted in transition and computation.