A method for dynamically configuring processes using a data cooperative having a plurality of data sources (150A-150C), the method comprising: receiving, by one or more processors, storage and execution policy information from a plurality of users, wherein the storage and execution policy information comprise criteria for storing user data and evaluating the user data in an execution environment; storing, by the one or more processors, the storage and execution policy information at a memory; periodically analyzing, by the one or more processors, the storage and execution policy information; generating, by the one or more processors, mapping data based on the periodic analyzing of the storage and execution policy information, the mapping data indicating a data privacy level for storage and use of user data obtained from the plurality of users in an execution environment; transmitting, by the one or more processors, the mapping data to a privacy preserving model management device adapted to configure the execution environment for applying one or more machine learning models to a dataset, wherein the execution environment is configured in a manner that satisfies the criteria of the storage and execution policy information; providing, by the one or more processors, a first portion of the dataset to the execution environment for evaluation by the one or more machine learning models, wherein the first portion of the dataset comprises user data captured by the one or more processors based on services provided to the plurality of users, and wherein a second portion of the dataset is provided to the execution environment by other data sources of the plurality of data sources of the data cooperative; and receiving, by the one or more processors, feedback based on the evaluation of the dataset by the one or more machine learning models, wherein the feedback comprises hyper parameters for configuring at least one machine learning model of the one or more machine learning models, and wherein the hyper parameters are configured based on training of the one or more machine learning models using the dataset. receiving, by one or more processors, storage and execution policy information from a plurality of users, wherein the storage and execution policy information comprise criteria for storing user data and evaluating the user data in an execution environment; storing, by the one or more processors, the storage and execution policy information at a memory; periodically analyzing, by the one or more processors, the storage and execution policy information; generating, by the one or more processors, mapping data based on the periodic analyzing of the storage and execution policy information, the mapping data indicating a data privacy level for storage and use of user data obtained from the plurality of users in an execution environment; transmitting, by the one or more processors, the mapping data to a privacy preserving model management device adapted to configure the execution environment for applying one or more machine learning models to a dataset, wherein the execution environment is configured in a manner that satisfies the criteria of the storage and execution policy information; providing, by the one or more processors, a first portion of the dataset to the execution environment for evaluation by the one or more machine learning models, wherein the first portion of the dataset comprises user data captured by the one or more processors based on services provided to the plurality of users, and wherein a second portion of the dataset is provided to the execution environment by other data sources of the plurality of data sources of the data cooperative; and receiving, by the one or more processors, feedback based on the evaluation of the dataset by the one or more machine learning models, wherein the feedback comprises hyper parameters for configuring at least one machine learning model of the one or more machine learning models, and wherein the hyper parameters are configured based on training of the one or more machine learning models using the dataset.