Is Your Progress Database GDPR Compliant?

Introducing DBlur: A Comprehensive Data Anonymization Tool for Progress Databases 

 

At Wayfare, we believe in innovation that protects your data and respects privacy, which is why we’re proud to introduce DBlur, a solution developed by our own team. Created to meet the growing demand for secure data handling in the era of GDPR, DBlur is an advanced anonymization tool specifically designed for Progress databases. At Wayfare, we understand the importance of safeguarding sensitive information and DBlur was crafted to do just that, ensuring data is protected while still remaining usable across non-production environments like testing and development.   

 

 

Why We Developed DBlur 

 

Before the GDPR era, production data was often copied across environments such as development and testing, which were not as secure as production environments. This raised the risk of data breaches. At Wayfare, we saw the need to help businesses anonymize sensitive data without disrupting operations. DBlur anonymizes the data, ensuring sensitive information remains protected while maintaining data usability across environments. 

DBlur anonymizes sensitive information in such a way that it remains usable for business processes, ensuring around 20% of the data is protected yet retains its functionality. 

For instance, anonymized and real data sets closely resemble each other, which ensures that your applications will perform consistently in all environments. We built DBlur to give organizations a reliable way to protect their data while still making it usable, effective and compliant. 

 

 

Key Features of DBlur 

 

Customizable Anonymization Rules:

 

DBlur offers a range of rules to anonymize data according to your business needs: 

  • Random Rule: Generates random values from a defined set, database table, or dictionary. 
  • Fixed Rule: Replaces the same value across fields. 
  • Encrypt Rule: Used for unique keys. 
  • Composed Rule: Combines anonymized values, such as constructing an email address using anonymized first and last names. 

 

Flexible Architecture:

 

Our product is structured to ensure flexibility and extensibility. New rules or parsers can easily be added to accommodate unique business needs. The product supports parallel processing, allowing for efficient handling of large datasets, making it ideal for overnight anonymization jobs. 

 

Dynamic Dictionaries:

 

DBlur dynamically loads dictionaries, which contain sets of values (e.g., street names or postal codes) to help anonymize data. The dictionaries can be easily updated and are configured in the tool’s settings, allowing for seamless integration with various datasets. 

 

Monitoring and Logging:

 

We built DBlur with a robust logging system, which provides detailed information about the anonymization process, identifies any errors, and ensures smooth troubleshooting and process tracking. This feature gives administrators complete control and insight into the anonymization workflow. 

 

 

How DBlur Works

 

The anonymization process in DBlur follows a structured workflow : 

  • Configuration Loading: The configuration file defines the environment, rules, and fields to be anonymized. 
  • Validation: Ensures that all configurations and rules are valid, and any discrepancies are flagged for correction. 
  • Anonymization: The tool applies the anonymization rules to the fields, ensuring data integrity and relations between tables remain intact. 
  • Exporting: The anonymized data is exported in a standard format that can be easily imported into test or development environments. 

 

Easy Installation and Integration 

 

We designed DBlur to be portable and easily installable in production environments. It does not require a dedicated database and uses temporary tables for dictionary loading and processing, making it lightweight and unobtrusive. 

For environments with large datasets, DBlur allows for parallel processing, speeding up the anonymization of massive data tables. For example, we managed to anonymize 17GB of data in just five hours, proving its efficiency for large-scale operations. 

 

A User-Friendly Interface 

 

DBlur also comes with a basic but functional user interface that allows users to upload configuration files, validate rules, and make adjustments with ease. Through the simplicity of the interface we ensure that even users without deep technical knowledge can operate the tool efficiently. 

 

Availability and Support 

 

At Wayfare, our commitment to customer success doesn’t end with the tool. We offer comprehensive support, training and consultation to help your organization seamlessly integrate DBlur into your workflow. Whether you’re working with vast production databases or smaller datasets, DBlur helps ensure that your sensitive information remains protected while keeping your Progress applications running smoothly. 

If you’re ready to see how DBlur can help you achieve GDPR compliance and keep your Progress database secure, get in touch with us at business@wayfare.ro to request a demo or more information on licensing. 

 

 

Let Wayfare be your trusted partner in data protection and compliance. 

 

 

 


 

Author: Cristina Truta, Tech Lead

Cristina is a smart and organized professional who brings energy and warmth to every situation. A wonderful mom and a joy to be around, she balances life with ease—and makes the best homemade chocolate you’ll ever taste!

 

 

Author: Mihaela Hatagan, People Experience Specialist

Ela is a curious and driven professional with a passion for learning and growth. A former programmer now thriving in human resources, she combines technical insight with a deep understanding of people, team dynamics, and company strategy. Always eager to take on challenges and support those around her, she brings valuable perspective and energy to every endeavor.

EMEA PUG Challenge 2019

EMEA PUG Challenge 2019

Last month, together with a few of #WayfarePeople, I had the pleasure of attending the annual EMEA PUG Challenge conference.

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