A computer system, the computer system comprising: a processing computer system, the processing computer system comprising a machine learning computer system; a plurality of computers (20a, 20b, 20c, 20d) each comprising a store, wherein each of the plurality of computers (20a, 20b, 20c, 20d) is configured to: provide one or more labels to replace determined data in documents stored in the respective store, wherein each label comprises one of: a private label and a public label; produce encoded documents including the one or more labels to replace the determined data in the documents; assign a routing token for directing the or each of the private labels to a private store of the machine learning computer system and the or each of the public labels to a public store of the machine learning computer system; and direct training data to the processing computer system based on the routing token, wherein the training data comprises the encoded documents; wherein the processing computer system is configured to process private labels and public labels and the private labels are interpretable only by the computer of the plurality of computers (20a, 20b, 20c, 20d) that provided the private label; and the public labels are interpretable by all of the plurality of computers (20a, 20b, 20c, 20d); wherein the machine learning computer system is configured to receive the encoded documents from the plurality of computers (20a, 20b, 20c, 20d) and to train a respective model for each of the plurality of computers (20a, 20b, 20c, 20d) based on the private labels and the public labels, the training comprising the machine learning computer system: using private labelled training data for training of only the model for the computer of the plurality of computers (20a, 20b, 20c, 20d) that provided the private label; and using public labelled training data for training the models for all of the plurality of computers (20a, 20b, 20c, 20d); and wherein each of the plurality of computers (20a, 20b, 20c, 20d) is configured to receive the respective model from the machine learning computer system, and wherein each of the plurality of computers (20a, 20b, 20c, 20d) comprises a respective trained model store for storing said received model. a processing computer system, the processing computer system comprising a machine learning computer system; a plurality of computers (20a, 20b, 20c, 20d) each comprising a store, wherein each of the plurality of computers (20a, 20b, 20c, 20d) is configured to: provide one or more labels to replace determined data in documents stored in the respective store, wherein each label comprises one of: a private label and a public label; produce encoded documents including the one or more labels to replace the determined data in the documents; assign a routing token for directing the or each of the private labels to a private store of the machine learning computer system and the or each of the public labels to a public store of the machine learning computer system; and direct training data to the processing computer system based on the routing token, wherein the training data comprises the encoded documents; provide one or more labels to replace determined data in documents stored in the respective store, wherein each label comprises one of: a private label and a public label; produce encoded documents including the one or more labels to replace the determined data in the documents; assign a routing token for directing the or each of the private labels to a private store of the machine learning computer system and the or each of the public labels to a public store of the machine learning computer system; and direct training data to the processing computer system based on the routing token, wherein the training data comprises the encoded documents; wherein the processing computer system is configured to process private labels and public labels and the private labels are interpretable only by the computer of the plurality of computers (20a, 20b, 20c, 20d) that provided the private label; and the public labels are interpretable by all of the plurality of computers (20a, 20b, 20c, 20d); wherein the machine learning computer system is configured to receive the encoded documents from the plurality of computers (20a, 20b, 20c, 20d) and to train a respective model for each of the plurality of computers (20a, 20b, 20c, 20d) based on the private labels and the public labels, the training comprising the machine learning computer system: using private labelled training data for training of only the model for the computer of the plurality of computers (20a, 20b, 20c, 20d) that provided the private label; and using public labelled training data for training the models for all of the plurality of computers (20a, 20b, 20c, 20d); and using private labelled training data for training of only the model for the computer of the plurality of computers (20a, 20b, 20c, 20d) that provided the private label; and using public labelled training data for training the models for all of the plurality of computers (20a, 20b, 20c, 20d); and wherein each of the plurality of computers (20a, 20b, 20c, 20d) is configured to receive the respective model from the machine learning computer system, and wherein each of the plurality of computers (20a, 20b, 20c, 20d) comprises a respective trained model store for storing said received model.