Step 1
Select the indirect identifiers to be released from the data set. 
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Select the variables at risk of re-identification. You can rank them in order of importance (the variables' utility to the person using the de-identified dataset). This ranking will be used during the de-identification process to determine the optimal anonymization that balances re-identification risk and data utility.
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Step 2 Set your re-identification risk threshold.
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To balance the need for privacy with the need for data resolution, PARAT allows you to set the acceptable re-identification risk threshold. Re-identification risk can be adjusted based on the profile of the person/organization requesting the information. Risk based de-identification ensures that individual privacy is protected while maintaining the released data's utility.
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Step 3 Perform the Risk Analysis
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PARAT calculates the dataset's risk for three types of re-identification attacks: prosecutor, journalist and marketer. In this example, PARAT shows the risk is high (above 0.2) for all three types of re-identification attacks (prosecutor, journalist, marketer).
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Step 4 De-identify to protect data
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PARAT uses several de-identification techniques including suppression (removing high risk records) and generalization (reducing the resolution of a given field). PARAT will automatically de-identify the data to reduce the re-identification risk to acceptable levels.
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