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Libelle DataMasking

Data masking software

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With Libelle DataMasking we provide data masking software to anonymize sensitive and personal data and work with it sensibly - whether in SAP® or another environment. Realistic-looking and GDPR-compliant data replaces the data in your non-production systems during anonymization.

You will be able to mask production data in a way that developers can easily access development or test systems and train participants. Analyses by experts remain meaningful despite anonymized data, as referential integrity is ensured after the data has been masked.

Your benefits

  • Privacy

    Protects against unwanted data access or even data theft on your non-production systems through anonymisation.

  • Realistic testing

    Libelle DataMasking delivers realistic-looking and logically correct data so you can test optimally.

  • Simplification

    Works on all common databases and can be carried out overnight using definable configurations.

Free Demo

You can try out the demo version of Libelle DataMasking all by yourself — for free & at any time!


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Libelle IT Blog
What is Test Data Management (TDM) actually about?
Success Story
Optimized data anonymization thanks to Libelle DataMasking

How Libelle DataMasking made data anonymization enormously easier for a customer in the banking industry. One billion data records consistently and securely anonymized in under six hours.

Libelle DataMasking is the data masking tool for anonymizing production data seaminglessly! It enables you to comply to the following legal frameworks:
  • Health Insurance Portability and Accountability Act (HIPAA)
  • Payment Card Industry Data Security Standard (PCI DSS)
  • Gramm Leach Bliley Act (GLBA)
  • Family Educational Rights and Privacy Act (FERPA)
  • Personal Information Protection and Electronic Documents Act (PIPEDA)
  • Personal Information Protection Law (PIPL)
  • EU General Data Protection Regulation (GDPR)
  • Federal Act on Data Protection (FADP)

Libelle Data­Masking

Learn more
Learn more


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Our data masking software works at database level, so we manage to anonymize up to 200,000 data entries per second. If that is not fast enough, tables can also be anonymized in parallel.

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Libelle DataMasking provides over 40 anonymisation algorithms and a reference database with defined target values as standard. However, if you wish to adjust your data masking software to your needs, you can also adapt everything here as required.

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We transform original production data into realistic-looking and logically correct data. You can thus realistically test, train, and evaluate on your non-production systems - without a concrete personal reference.

Icon Flexible - Computer Tab with two databases in the background


Libelle DataMasking can access any common database and be used as a data masking tool for individual systems or complete system landscapes. Databases in different environments are recognized and anonymized across systems.

How it works

We are constantly developing our data masking software and responding to the needs of our customers. With the latest release, we have introduced a test run before the actual work steps. This simulates a complete anonymization down to the actual changes in the data. This allows you to test all critical processes in advance and correct errors before you let the anonymization run automatically overnight or over the weekend.

Libelle DataMasking frontend shows the check-phase

Step 1: Inspection

In the check phase, Libelle DataMasking checks the infrastructure of your IT and whether the target system is available. Especially for SAP environments, it ensures that the system is not on production. This is done fully automatically and is an important step in order to take full advantage of our data masking software. Errors or fields that were not recognized can then be easily corrected.

Libelle DataMasking frontend shows the pre-phase

Step 2: Preparation

After the successful check, the next step is the pre-phase. Here, the supplied reference files are provided and the keys for anonymization are generated. If necessary, you can also create backup tables here. This is a further facilitation that comes with our data masking software before your first anonymization. If this has not been done according to your wishes, you can fall back on the backups.

Libelle DataMasking frontend shows the anon-phase

Step 3: Anonymization

In the anon phase, the supplied and individually set anonymization algorithms are applied. These read the data from the non-production target system and anonymize it with the references provided. The result is realistic-looking and GDPR-compliant data.

Libelle DataMasking frontend shows the post-phase

Step 4: Review

In the last step, the post phase, it is checked whether the consistency of the data has been maintained, a final measure to obtain an optimal result. To give you a complete overview of the anonymization, Libelle DataMasking provides you with a comprehensive final report with all relevant information.

Technical data

Excerpt of the anonymization algorithms

  • Addresses
  • Dates and times
  • E-Mail addresses
  • IBANs
  • Names
  • Numbers
  • Organisations
  • Regions

Provided SAP templates

  • SAP SD

Supported databases

  • IBM DB2
  • MariaDB
  • Microsoft SQL Server*
  • MySQL
  • Oracle
  • PostgreSQL
  • SAP MaxDB

We also support CSV, JSON and XML file formats.

Supported operating systems

  • AIX
  • HP-UX
  • Linux
  • MacOS
  • Solaris
  • Windows

Cloud availability

With Libelle DataMasking we provide data masking software that is cloud-ready and can be deployed as a machine image at the following providers:

* Support on Windows and PowerPC only

Frequently Asked Questions

How long does an anonymization take with your data masking software?

Which databases does Libelle DataMasking support?

Can I also repeat an anonymization?

Is it possible to run Libelle DataMasking on-cloud?

How easy is it to customize the standard workflow of your data masking software?

We are in constant exchange with our customers and curiously observe the handling of data and how it will develop. As soon as an innovation or adaptation has proven itself, it is incorporated into our algorithms.