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MACHINE LEARNING BASED ON HOMOMORPHIC ENCRYPTION

机译:基于同构加密的机器学习

摘要

A method for evaluating data (28) is based on a computational model, the computational model comprising model data (26), a training function and a prediction function. The method comprises training the computational model by: receiving training data (22) and training result data (24) for training the computational model, and computing the model data (26) from the training data (22) and the training result data (24) with the training function. The method comprises predicting result data (30) by: receiving field data (28) for predicting result data (30); and computing the result data (30) from the field data (28) and the model data (26) with the prediction function. The training data (22) may be plaintext and the training result data (24) may be encrypted with a homomorphic encryption algorithm, wherein the model data (26) may be computed in encrypted form from the training data (22) and the encrypted training result data (24) with the training function. The field data (28) may be plaintext, wherein the result data (30) may be computed in encrypted form from the field data (28) and the encrypted model data (26) with the prediction function.
机译:用于评估数据的方法(28)基于计算模型,该计算模型包括模型数据(26),训练函数和预测函数。该方法包括通过以下步骤来训练计算模型:接收用于训练计算模型的训练数据(22)和训练结果数据(24);以及从训练数据(22)和训练结果数据(24)计算模型数据(26)。 )具有训练功能。该方法包括通过以下步骤来预测结果数据(30):接收用于预测结果数据(30)的字段数据(28);并利用预测功能从现场数据(28)和模型数据(26)计算结果数据(30)。训练数据(22)可以是纯文本,并且训练结果数据(24)可以用同态加密算法加密,其中可以从训练数据(22)和加密的训练中以加密形式计算模型数据(26)。具有训练功能的结果数据(24)。现场数据(28)可以是纯文本,其中,结果数据(30)可以以具有预测功能的方式从现场数据(28)和加密的模型数据(26)以加密形式计算。

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