Secure computation, a methodology of computing on encrypted data, has become a key factor in machine learning. Homomorphic encryption (HE) enables computation on encrypted data without leaking any information to untrusted servers. In machine learning, the model selection method is a crucial algorithm that determines the performance and reduces the fitting problem. Despite the importance of finding the optimal model, none of the previous studies have considered model selection when performing data analysis through the HE scheme. The HE-based model selection we proposed finds the optimal complexity that best describes given data that is encrypted and whose distribution is unknown. Since this process requires a matrix calculation, we constructed the matrix multiplication and inverse of the matrix based on the bitwise operation. Based on these, we designed the model selection of the HE cross-validation approach and the HE Bayesian approach for homomorphic machine learning. Our focus was on evidence approximation for linear models to find goodness-of-fit that maximizes the evidence. We conducted an experiment on a dataset of age and Body Mass Index (BMI) from Kaggle to compare the capabilities and our model showed that encrypted data can regress homomorphically without decrypting it.
Bibliographical noteFunding Information:
Acknowledgments: This study was supported by the Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (no. 2017-0-00545).
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Bitwise operation
- Cross validation
- Evidence approximation
- Fully homomorphic encryption
- Gauss-jordan elimination
- Model selection
ASJC Scopus subject areas
- Materials Science(all)
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes