Method, apparatus and computer program

A method comprising:

• storing a dataset comprising first data and second data for at least one User Equipment, wherein the first data has been provided to a network function and the second data has not been provided to the network function, and wherein the network function trains a machine learning model based on the first data;
• storing ground truth labels for the first data and the second data;
• sending information based on the dataset to the network function;
• receiving, from the network function, information for determining a first value of a loss function, the first value of the loss function indicating a difference between the ground truth labels of the first data and at least one predicted ground truth label of the first data determined by the network function using the machine learning model;
• receiving, from the network function, information for determining a second value of the loss function, the second value of the loss function indicating a difference between the ground truth labels of the second data and at least one predicted ground truth label of the second data determined by the network function using the machine learning model;
• determining a difference between the first value of the loss function and the second value of the loss function;
• and
• comparing the difference to a threshold to determine whether a Differential Privacy mechanism was applied to the first data before the network function trained the machine learning model based on the first data.

A non-transitory computer readable medium comprising program instructions that, when executed by an apparatus, cause the apparatus to perform at least the following:

• storing a dataset comprising first data and second data for at least one User Equipment, wherein the first data has been provided to a network function and the second data has not been provided to the network function, and wherein the network function trains a machine learning model based on the first data;
• storing ground truth labels for the first data and the second data;
• sending information based on the dataset to the network function;
• receiving, from the network function, information for determining a first value of a loss function, the first value of the loss function indicating a difference between the ground truth labels of the first data and at least one predicted ground truth label of the first data determined by the network function using the machine learning model;
• receiving, from the network function, information for determining a second value of the loss function, the second value of the loss function indicating a difference between the ground truth labels of the second data and at least one predicted ground truth label of the second data determined by the network function using the machine learning model;
• determining a difference between the first value of the loss function and the second value of the loss function;
• and
• comparing the difference to a threshold to determine whether a Differential Privacy mechanism was applied to the first data before the network function trained the machine learning model based on the first data.