Abstract
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.
Highlights
Due to the continuous increase in computational power and the rapid development of processing and storage technologies, people use cloud computing to manage and analyze massive amounts of biomedical data generated from many sensors
Since all the data resides with the cloud server, a critical data issue arises if the cloud provider misuses the information
We introduced isomorphic logarithms and Homomorphic encryption (HE) logarithms used for Bayesian model selection
Summary
Due to the continuous increase in computational power and the rapid development of processing and storage technologies, people use cloud computing to manage and analyze massive amounts of biomedical data generated from many sensors. One of the best ways to guarantee data privacy is to encrypt data before sending it to the cloud server. There is still the problem of the cloud servers calculating the encrypted data to respond to the request from the client. The client needs to offer the secret key to the server to decrypt the data before executing the calculations, which could lead to breach of confidentiality or invasion of privacy. Sci. 2020, 10, 6174 applied to machine learning to find useful information from big data. We proposed two ways of the first HE based model selection: Homomorphic cross-validation (HCV) and Homomorphic Bayesian model selection (HBMS), to determine the complexity of models that can explain the data well when given the encrypted data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
