Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption
- Published
- Source
- arXiv
- Paper number
- 322
- Field
- Machine Learning
- arXiv ID
- 2606.05129
Key points
- This paper proposes a fully homomorphic encryption (FHE)-based method that keeps data encrypted during transmission and computation by performing calculations on ciphertexts.
- It also shows that the method can be portably extended to support differential privacy, making it easy to scale beyond FHE.
- Experimental results show that the method achieves high consistency and comparable causal structures to the plaintext version on the tested datasets.
Paper links
External research summaries. These are not HDATF publications or measured product results.