For data anonymization, choose Gedis Studio

Data Anonymization, Data Masking with GEDIS Studio

Why High Quality Data Anonymization Matters

Data masking is one of the most popular approach to live data anonymization prior to populating  testing or training environments. By replacing sensitive data with fake data you will be able to disclose your production data in various test environments, even outside of your organization. However, ineffective data masking may result in poor quality data, useless for replacing sensitive real data.

When it comes to data quality and realism, GEDIS Studio is the best tool on the market. It is not another database spying tool; it is a tool for producing replacement data.

Data anonymization cannot be reduced to just cleaning sensitive data: the resulting data need to be as goog as the original data

Pitfall #1 in Data Anonymization : Context Free Data Masking

Context-free data masking means that the replacement data are synthesized without regard to target context, application constraints or the test cases the data are intended for. Context free data masking often occurs when those driving the data anonymization are also responsible for the data management application, or are a dedicated support team, rather than the end-users of the data. Removing sensitive data is only one aspect of the problem: the resulting data must also be realistic.

Solution : Known What You Need

When it comes to data scrambling or masking and any other data anonymization techniques, start with a clear definition of the data quality you need and communicate it to those in charge of producing your anonymized test data. Make sure the initial data sample is of the required size and quality and that these properties are not removed in the course of data anonymization. A good test data anonymization process is driven by the needs of your test strategy and test cases, which should formalize and aggregate test data requirements.

Discover how GEDIS Studio can reinforce your Anonymization Process

Watch our videos to see how GEDIS Studio provides efficient support for various data masking techniques in a Customer Relationship Management (CRM) application.

Production Data Encryption

Data encryption anonymizes data by replacing sensitive data by encrypted data. Depending on the type of encryption you use, you can expect the process to be reversible.

Pros: data encryption is easy to put in place and provides a good anonymization process since it is almost impossible to revert to the original data without knowledge of the encryption key and encryption algorithm.

Cons: Data encryption transform data into unreadable data. For example, if you encrypt names like "John" you will obtain something like "geyFSre4". It is ok from a anonymization point of view, but renders data unsuitable for the test or training environment.

GEDIS Studio encryption rules are well adapted to data encryption for anonymization purpose. See the demo.

Production Data Masking

Masking data with substitution values is probably the most widespread way of anonymizing production data to obtain data for testing purpose. It can also be the worst in term of the quality of the anonymized data if you do not pay special attention to producing realistic data.

GEDIS Studio is dedicated to this purpose: let our data masking video convince you to produce substitution values!

Reversible Data Anonymization

Anonymization reversibility is crucial if you want to be able to retreive the original data from the masked data. GEDIS Studio encoding/decoding mechanism is ideally suited for data de-anonymization.

Consistent Data Anonymization

Consistent anonymization means that if you anonymize a given value for a given field you will always replace it by the same value. Consistency can be context sensitive or not. Context sensitive consistency means that both the value and the field are considered when producing a substitution value, whereas context insensitive means that only the value is considered and thus the substitution value remains always the same wherever it is applied.

GEDIS Studio provides both context sensitive and insensitive anonymization techniques.