Privacy Preserving Neural Network Models based Homomorphic Encryption : A Case Study for Diabetes Prediction
Marcel Antal, Daria Miu, Alexandru Rancea · 2023
This paper addresses the problem of data privacy in distributed applications using machine learning models in the context of modern privacy laws, such as GDPR, by proposing a machine learning model hybridized to use homomorphic operators to perform model evaluations on predicted data. The paper presents the general approach of building such a model using frameworks such as TesSeal, Keras, Microsoft SEAL, and their integration in a web-distributed application. Due to the complexity of the homomorphic operations, only the prediction is proposed to be executed on encrypted data. An experimental platform is developed where a machine learning algorithm is trained to predict diabetes based on laboratory tests. The proposed models that use homomorphic operation to run on encrypted data are evaluated and compared to the same models that operate on plain-text data to determine the cost of encrypting the data from the point of view of model accuracy and execution time. Experiments show that both the model that runs on encrypted data and the model that runs on plain data have similar accuracy. However, the execution time of the model that runs on encrypted data is 10 to 20 times higher than of the model using plain text data.