TY - JOUR
T1 - Machine Learning with Partially Homomorphic Encrypted Data
AU - Muhammad, K.
AU - Sugeng, K. A.
AU - Murfi, H.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - Machine learning had been widely used to analyze various kinds of data, including sensitive data such as medical and financial data. A trained machine learning model can be wrapped in a web application so that people can access it easily via internet. However, if the data to be analyzed is private or confidential, this will cause a problem; the application administrator may read the input. As shown by Dowlin et al. in their remarkable paper, this kind of problem can be solved with homomorphic encryption scheme. Paillier encryption scheme is one kind of encryption scheme that has homomorphic property. In this research, we will show that one type of machine learning model can take an input encrypted by Paillier encryption scheme and produce an encrypted output that shares the same key. A machine learning model will be trained with the MNIST database of hand-written digits. This model will be tested with the test data encrypted with Paillier encryption scheme. The experiment shows that the model achieved 92.92% accuracy on the test set.
AB - Machine learning had been widely used to analyze various kinds of data, including sensitive data such as medical and financial data. A trained machine learning model can be wrapped in a web application so that people can access it easily via internet. However, if the data to be analyzed is private or confidential, this will cause a problem; the application administrator may read the input. As shown by Dowlin et al. in their remarkable paper, this kind of problem can be solved with homomorphic encryption scheme. Paillier encryption scheme is one kind of encryption scheme that has homomorphic property. In this research, we will show that one type of machine learning model can take an input encrypted by Paillier encryption scheme and produce an encrypted output that shares the same key. A machine learning model will be trained with the MNIST database of hand-written digits. This model will be tested with the test data encrypted with Paillier encryption scheme. The experiment shows that the model achieved 92.92% accuracy on the test set.
UR - http://www.scopus.com/inward/record.url?scp=85058345113&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1108/1/012112
DO - 10.1088/1742-6596/1108/1/012112
M3 - Conference article
AN - SCOPUS:85058345113
VL - 1108
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012112
T2 - 2nd Mathematics, Informatics, Science and Education International Conference, MISEIC 2018
Y2 - 21 July 2018
ER -