If you plan to make production data available for development and test purposes, you'll need to understand which columns contain personal or sensitive data, create a data catalog to record those decisions, devise and implement a data masking, and then provision the sanitized database copies. Richard Macaskill show how to…Read more
Grant Fritchey shows how to provision a group of interdependent databases, masked to protect sensitive or personal data, to each machine in an Azure-based test cell.Read more
Grant Fritchey shows how to adapt a data masking process, for address data, so that it incorporates knowledge of the data distribution in the real data. The result is fake address data, with an accurate distribution, for use in development and testing work.Read more
Grant Fritchey provides a simple way to create fake address information that still looks real. The compromise is that it uses random data distributions and doesn't maintain any correlation between postal codes, states and cities, so won’t accurately reflect the real address data.Read more
Grant Fritchey shows how to use Data Masker to create fake credit card data that not only looks like the real thing, but also has the right distribution, so will act like it too, when we query it.Read more
Steve Jones show how a team might use SQL Provision to build consistent, compliant, useful databases, on demand, for development and test environments.Read more
Steve Jones shows a simple way to provision full size databases for developers, using production like data that has been masked automatically as part of the provisioning process.Read more
This article takes a strategic look at common data masking and anonymization techniques, and the challenges inherent in protecting certain types of sensitive and personal data, while ensuring that it still looks like the real data, and retains its referential integrity, and distribution characteristics. It also explains, briefly, with references,…Read more
Chris Unwin explains the basic approaches to anonymizing email addresses, and shows how Data Masker can generate realistic email addresses, based on faked names, and even retain the correct distribution of email providers.Read more