Stambia Privacy Protect

Discover how Stambia's Privacy Protect module protects your personal data and meets regulatory requirements (GDPR, CNIL3), due to its features:

  • for anonymization and pseudonymization of data
  • for filling in databases from generated data
  • for traceability and audit

"Privacy by design" natively integrated into your integration solution

Use cases of the Privacy Protect component

Replicating a data model with anonymization and/or pseudonymization

The case of anonymization

Anonymisation

Data protection can consist in anonymizing development or test databases, by loading them from the real data of the production databases. Stambia Privacy Protect allows you to define the rules on metadata (tables, columns, etc.) and then perform the specified anonymization.

The case of pseudonymization

Pseudonymization may also be necessary to protect your data for analysis purposes: 

  • Expose data to the outside (Open data)
  • Set up Analytics systems (Business Intelligence, Big Data, Machine learning)
Pseudonymisation

Feeding a new database with randomly generated datasets

NewDataSet

The development of new business applications or simply new business intelligence data models will require the creation of data sets during the development and testing phases. Stambia Privacy Protect automatically feeds new databases.

Certify the methods of protecting your personal data and compliance with the GPDR

The obligation of proof is one of the constraints of the GPDR. Every organization must show that it has taken the subject of privacy as part of its daily life. Stambia Privacy Protect makes it easy to set up audits and certifications, especially by automatic reports generated during any use of the component. 

Stambia Privacy Protect log

How does the component work ?

A component integrated into Metadata

The Privacy Protect component is fully integrated into the data integration solution, through Stambia's "Metadata". For each schema, table, column, the user can define his rules regarding anonymization, pseudonymization or data generation. These rules are then applied when the component is executed with the configured metadata.

How Metadata
 

A complete range of functionalities

Several protection methods

  • Substitution by provided or customized dictionaries
  • Deduction from these dictionaries
  • Generation of random values, automatic numbering
  • Obfuscation, encryption, SQL transformation
  • Generation of temporary correspondence tables, pseudonymization tables
Stambia Privacy Protect Method List

Advanced features for these methods

  • Several data distribution methods: taking into account (or not) the uniform distribution of data (uniform, by weight in the dictionary or by normal law)
  • Intelligent processing of zero values
  • Use of personalized masks (emails, phones,...)
  • Taking into account the geographical particularities of your data
  • Building HTML reports (anonymization/pseudonymization log, samples)
  • Use of database engines (like Oracle, PostgreSQL)
  • Loading empty tables from Privacy Protect methods

 

Technical specification and prerequisites

SpecificationDescription

Audit / traceability

Reports in HTML format as well as in database
Supported encryption methods
  • DES,
  • 3DES_2KEY, 3DES,
  • MD5,
  • AES128, AES256, AES192,
  • BLOWFISH,
  • SHA1, SHA224, SHA256, SHA384, SHA512
Databases
  • Oracle
  • PostgreSQL
  • Other databases will be added soon (please contact us)
Stambia Designer Version Stambia Designer s19.1
Stambia Runtime Version Stambia Runtime S17.5.6
Notes complémentaires
  • Development environment: any system capable of supporting Stambia Designer (Windows, MacOS, Linux)
  • Runtime environment: any system capable of supporting a Java environment

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