Prediction of acute lymphoid leukemia using Privacy Preserving Neural Network

Abstract

In today’s world, machine learning has become a big factor. It not only needs to be helpful, but also accurate and precise prediction is required. Machine learning is now becoming a widely used mechanism and applying it in certain sensitive fields like medical and financial data has only made things easier, but it also brought some difficulty in data privacy and data security which will protect the complete implementation of cloud based machine learning for these aspects due to the law and ethical needs. In this project, to give proper solution, we have come up with the idea using concepts of CryptoNets and Neural Networks, where we will be able to convert the learned neural network with the encrypted data to Cryptonets and the data will be totally encrypted and this will prevent the chances of unencrypted data being available to everyone. In this method, the owner will send the encrypted data to the cloud first and will hold a private key which can be used to decrypt the data later on. The cloud will have no idea about the data there since it will be in encrypted form and any attempts to get data from the cloud will only give the encrypted form. However, applying neural network to the cloud will enable us to store the data and make predictions in encrypted form and also give back the encrypted data to the user. In this way, the cloud will have no idea about the actual data and after the prediction is made, it will give back the predicted data in the encrypted form. We were able to achieve an encrypted prediction of about 78% close to the validation accuracy amount we achieved when training our Neural Network model.