Google Sheets

Problem

Businesses want to run tests and statistical analysis on existing Google Sheets containing sensitive customer data. That sensitive data needs to be replaced with fake data (aka format-preserving pseudonyms) for the following reasons:

• Productivity: Manually generating pseudonyms is time consuming work and is prone to typos or semantic errors that make the generated data unusable.
• ‍Compliance: GDPR, HIPAA, CCPA and many other privacy laws enforce all customer sensitive data to be accessed on a need-to-know basis. Removing data access for testing and analytic workload reduces compliance risk.
• Security: Sharing updated sheets allow recipients to still access sensitive customer data because Google Sheets maintain a version history that tracks all changes. Also, insider attacks are common where employees who have access to customer's sensitive data lead to data exfiltration.

Solution

Strac Google Sheets Anonymizer is a Data Loss Prevention (DLP) software. It creates a copy of the original Google Sheets with sensitive data elements replaced using format-preserving pseudonyms. For developers building applications backed by Google Sheets data, it allows them to test their code using data that look the same as production. For business analysts, it allows them to perform statistic analysis on models that have the same properties as production data. All without seeing the sensitive data.

Users can configure a list of sensitive data elements to anonymize. Below is a sample list of data elements that can be converted to pseudonyms:

• ‍Financial: Bank (Account Name, Routing Number, Account Number, PIN, BIC, IBAN), Crypto (Bitcoin Address, Ethereum Address), Credit/Debit Card (Account Number, CVV, Issuer), Vehicle Identification Number
• Tracking: Address (Street, City, State, Zip Code), Date, Email, IP Address, Job Title, Mac Address, Phone Number
• Identification: Age, Gender, Income, Name, User Name, Password, SSN