TL;DR: Strac - A Comprehensive Solution for Pseudonymization
- Strac Google Sheets Pseudonymization replaces sensitive customer data with format-preserving pseudonyms for productivity, compliance, and security reasons.
- It is a DLP software that creates a copy of the original Google Sheets with anonymized data elements.
- It allows developers and business analysts to test and analyze data without seeing sensitive information. Users can learn how to pseudonymize data in Excel by configuring a list of sensitive data elements to anonymize with Strac.Users can configure a list of sensitive data elements to anonymize, including financial, tracking, and identification data.
- Strac pseudonymizes (aka removes) PII, PCI or PHI from CSV files, Google Sheets, or Microsoft Excel files.
- See our video demo below to learn about how Strac Google Sheets DLP and how masking/pseudonymization works
Identifying the Problem: Why Pseudonymization is Important for Data Privacy
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.
The Solution: Step-by-Step Guide on Pseudonymizing Data in Excel or Google Sheets or CSV with Strac
Strac Google Sheets Pseudonymization 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. Learn how to pseudonymize data in Excel and remove sensitive data from CSV files, Google Sheets, or Microsoft Sheets. This process is particularly useful for business analysts who want to perform statistical analysis on models that have the same properties as production data without compromising sensitive information. It essentially removes PII or PHI or any sensitive data from CSV file, Google Sheets or even Microsoft Sheets.
Users can configure a list of sensitive data elements to pseudonymize. Below is a sample list of data elements that can be converted to pseudonyms:
- BANK_ACCOUNT_NUMBER
- BANK_ROUTING_NUMBER
- BANK_PIN
- BANK_IDENTIFIER_CODE
- BITCOIN_ADDRESS
- CITY
- CREDIT_CARD_NUMBER
- CREDIT_CARD_CVV
- CREDIT_CARD_ISSUER
- DATE
- ETHEREUM_ADDRESS
- FINANCIAL_ACCOUNT_NAME
- FINANCIAL_ACCOUNT
- GENDER
- INTERNATIONAL_BANK_ACCOUNT_NUMBER
- IP_ADDRESS
- JOB_TITLE
- MAC_ADDRESS
- NAME
- NUMBER
- PASSWORD
- PHONE_NUMBER
- ROUTING_NUMBER
- SALARY
- STATE
- STREET_ADDRESS
- SOCIAL_SECURITY_NUMBER
- USER_NAME
- VEHICLE_IDENTIFICATION_NUMBER
- ZIP_CODE
