Data anonymization based on guessing anonymity

An apparatus comprising:

• a memory to store instructions;
• and
• a processor to execute the instructions to:
• invoke an anonymization function provided via an application interface,
• the anonymization function being invoked through an activation of a user input mechanism of the application interface;
• receive, based on invoking the anonymization function, a selection of a noise model to be applied to data;
• determine a first anonymization parameter,
• the first anonymization parameter comprising an expected distortion;
• determine a particular level for the first anonymization parameter;
• the determined particular level being a maximum expected distortion level;
• determine a second anonymization parameter based on the first anonymization parameter,
• the second anonymization parameter being an expected guessing anonymity,
• the expected guessing anonymity being a number of guesses needed to determine an original record from a sanitized record;
• determine, based on determining the particular level for the first anonymization parameter, a parameter value for the noise model that optimizes the second anonymization parameter;
• generate noise based on the noise model and the determined parameter value;
• apply the generated noise to the data;
• generate noise perturbed data based on applying the generated noise to the data,
• the noise perturbed data having an anonymized structure, and
• the noise perturbed data being associated with:
• the determined particular level with respect to the expected distortion, and
• the determined parameter value with respect to the expected guessing anonymity;
• and
• provide the noise perturbed data to a destination.

A non-transitory computer readable medium storing instructions, the instructions comprising:

• one or more instructions which, when executed by a processor, cause the processor to:
• invoke an anonymization function provided via an application interface,
• the anonymization function being invoked through an activation of a user input mechanism of the application interface;
• receive, based on invoking the anonymization function a selection of a noise model to be applied to data;
• determine a first anonymization parameter;
• the first anonymization parameter comprising an expected distortion;
• determine a particular level for the first anonymization parameter;
• the determined particular level being a maximum expected distortion level;
• determine a second anonymization parameter based on the first anonymization parameter,
• the second anonymization parameter being an expected guessing anonymity,
• the expected guessing anonymity being a number of guesses needed to determine an original record from a sanitized record;
• determine, based on determining the particular level for the first anonymization parameter, a parameter value for the noise model that optimizes the second anonymization parameter;
• generate noise based on the noise model and the determined parameter value;
• apply the generated noise to the data;
• generate noise perturbed data based on applying the generated noise to the data,
• the noise perturbed data being associated with:
• the determined particular level with respect to the expected distortion, and
• the determined parameter value with respect to the expected guessing anonymity;
• and
• provide the noise perturbed data to a destination.