The Consent Management Reasoning Tool utilizes a machine learning model to provide policy recommendations to the user. The model is based on a Markov Logic Network where each policy is assigned a weight of importance based on the active policies of all the users and the (transfer) requests logs. By assigning an importance weight on each policy we can provide recommendations to the user if the weight passes a specific threshold. This essential means that as more users are active on the system the more accurate the recommendations are. Is worth mentioning that the threshold of each attribute is defined by the IDC's administrator. However due to the computational requirements of training the model, we resort to offline process. The consent policy recommendations and their associate weights are computed in weekly intervals or when enough requests are made that render the re-training essential.
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