Data Anonymization

[BJ]

Hi,

What capabilities are provided by Paxata to Anonymize customer data?

The customer data can be provided by on premise customer in flat files.

Regards,

Bhanu

[akshay]

DataRobot DataPrep has multiple guardrail options which can help admins make sure only certain people/groups have access to the data.

We can also build DataPrep projects and implement Find + Replace to mask certain rows and columns of data. Please do let me know if this helps or if you need any additional information regarding the same.

Thanks,

Akshay

[BJ]

Can you add more to find and replace capability for anonymization. 

Is it limited to replacing the values with null values or some junk or dummy patters $$$$  or ######.

Does the attribute maintains it's intrinsic behavior while anonymizing the column?

Do you also have anonymization approaches to maintain referential integrity?

[akshay]

Could you give me an example for the kind of anonymization that you're trying to do?

[BJ]

Hi, These are the Data Anonymization techniques. What can we do in Paxata to anonymize data?

§Data masking – Substitute the characters with Null values, * or #

§Pseudonymization – Replacing with similar false identity, replacing dummy name

§Generalization – Remove specific identifiers, retain only Year of Date of Birth

§Synthetic Data Generation – System generated synthetic values by applying statistical functions (mean, median)

Can we build custom scripts (e.g. in SQL, Python)  in Paxata to perform anonymization on datasets.

[akshay]

All of these things can be done on a DataPrep project - once this is done you can give access to the project output dataset to the users for their dataprep exercise.

1) Data Masking - use Find and replace on Paxata - mask the characters that you are looking to and replace them with Null,* or 

2) Pseudonymization - you can do a look up that will map all the names in your data with dummy names.

3) Generalization - can be done using computed columns for the date of birth example that you just mentioned (Different Generalizations will have different approaches)

4) Synthetic data generation - you can use the mean/median compute function to populate a column with just mean/median values and just replace the values that you are looking to replace using find+replace.

Note: All of this have to be manually done once by an admin user before they can be automated.

and the output of these projects would contain the anonymized data.∫

As of today, we do not support python or SQL transformations on Paxata. However, we our working on getting Spark SQL data transformation in our product in the near future.