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<\/span><\/div><\/div><\/div><\/div><\/div>Data masking and anonymization:
\nunderstanding the different algorithms<\/h2><\/div>Data masking and anonymization are essential pillars of sensitive data protection, especially under the GDPR. These techniques modify datasets to make them unidentifiable while preserving their analytical or testing value.<\/p>\n
According to the CNIL (French Data Protection Authority), anonymization aims to make it impossible to identify an individual from their data.<\/p>\n
In this article, we explore the main data masking algorithms used to protect information while maintaining data consistency and usefulness.<\/p>\n<\/div>
Types of Data Masking Algorithms<\/h2><\/div>1. Substitution Algorithms: Preserving a Realistic Appearance<\/h3><\/div>With substitution algorithms, specific data fields are replaced by other values. The resulting information still appears real but allows anonymization and protection of individuals\u2019 identities in the dataset.<\/p>\n
Example:<\/b><\/p>\n<\/div>Original dataset<\/h4><\/div>Name:<\/b> Brown \u2013 Salary:<\/b> 95,000
Name:<\/b> Smith \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/i><\/div><\/div><\/i><\/div><\/div>Anonymized dataset<\/h4><\/div>Name:<\/b> Green \u2013 Salary: <\/b>95,000
Name:<\/b> Jones \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/div>2. Randomization Algorithms: Shuffling Data<\/h3><\/div>This algorithm randomly rearranges characters within each column, making it very difficult to reconstruct the original information.<\/p>\n
Example :<\/b><\/p>\n<\/div>Original dataset<\/h4><\/div>Name:<\/b> Brown \u2013 Salary:<\/b> 95,000
Name:<\/b> Smith \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/i><\/div><\/div><\/i><\/div><\/div>Anonymized dataset<\/h4><\/div>Name:<\/b> Worbn \u2013 Salary:<\/b> 95,000
Name:<\/b> Miths \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/div>3. Numeric Variation Algorithms: Generating Realistic Data<\/h3><\/div>By applying numeric or date variation algorithms, it is possible to create a fictitious dataset derived from the original numerical information. By defining a meaningful variation range (e.g. \u00b110%), you can produce results close to reality while making it impossible to retrieve the original dataset.<\/p>\n
Example :<\/b><\/p>\n<\/div>Original dataset<\/h4><\/div>Name:<\/b> Brown \u2013 Salary: <\/b>95,000
Name:<\/b> Smith \u2013 Salary:<\/b> 125,000<\/p>\n<\/div><\/div><\/div><\/i><\/div><\/div>
Data masking and anonymization are essential pillars of sensitive data protection, especially under the GDPR. These techniques modify datasets to make them unidentifiable while preserving their analytical or testing value.<\/p>\n
According to the CNIL (French Data Protection Authority), anonymization aims to make it impossible to identify an individual from their data.<\/p>\n
In this article, we explore the main data masking algorithms used to protect information while maintaining data consistency and usefulness.<\/p>\n<\/div>
Types of Data Masking Algorithms<\/h2><\/div>1. Substitution Algorithms: Preserving a Realistic Appearance<\/h3><\/div>With substitution algorithms, specific data fields are replaced by other values. The resulting information still appears real but allows anonymization and protection of individuals\u2019 identities in the dataset.<\/p>\n
Example:<\/b><\/p>\n<\/div>Original dataset<\/h4><\/div>Name:<\/b> Brown \u2013 Salary:<\/b> 95,000
Name:<\/b> Smith \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/i><\/div><\/div><\/i><\/div><\/div>Anonymized dataset<\/h4><\/div>Name:<\/b> Green \u2013 Salary: <\/b>95,000
Name:<\/b> Jones \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/div>2. Randomization Algorithms: Shuffling Data<\/h3><\/div>This algorithm randomly rearranges characters within each column, making it very difficult to reconstruct the original information.<\/p>\n
Example :<\/b><\/p>\n<\/div>Original dataset<\/h4><\/div>Name:<\/b> Brown \u2013 Salary:<\/b> 95,000
Name:<\/b> Smith \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/i><\/div><\/div><\/i><\/div><\/div>Anonymized dataset<\/h4><\/div>Name:<\/b> Worbn \u2013 Salary:<\/b> 95,000
Name:<\/b> Miths \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/div>3. Numeric Variation Algorithms: Generating Realistic Data<\/h3><\/div>By applying numeric or date variation algorithms, it is possible to create a fictitious dataset derived from the original numerical information. By defining a meaningful variation range (e.g. \u00b110%), you can produce results close to reality while making it impossible to retrieve the original dataset.<\/p>\n
Example :<\/b><\/p>\n<\/div>Original dataset<\/h4><\/div>Name:<\/b> Brown \u2013 Salary: <\/b>95,000
Name:<\/b> Smith \u2013 Salary:<\/b> 125,000<\/p>\n<\/div><\/div><\/div><\/i><\/div><\/div>
1. Substitution Algorithms: Preserving a Realistic Appearance<\/h3><\/div>With substitution algorithms, specific data fields are replaced by other values. The resulting information still appears real but allows anonymization and protection of individuals\u2019 identities in the dataset.<\/p>\n
Example:<\/b><\/p>\n<\/div>Original dataset<\/h4><\/div>Name:<\/b> Brown \u2013 Salary:<\/b> 95,000
Name:<\/b> Smith \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/i><\/div><\/div><\/i><\/div><\/div>Anonymized dataset<\/h4><\/div>Name:<\/b> Green \u2013 Salary: <\/b>95,000
Name:<\/b> Jones \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/div>2. Randomization Algorithms: Shuffling Data<\/h3><\/div>This algorithm randomly rearranges characters within each column, making it very difficult to reconstruct the original information.<\/p>\n
Example :<\/b><\/p>\n<\/div>Original dataset<\/h4><\/div>Name:<\/b> Brown \u2013 Salary:<\/b> 95,000
Name:<\/b> Smith \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/i><\/div><\/div><\/i><\/div><\/div>Anonymized dataset<\/h4><\/div>Name:<\/b> Worbn \u2013 Salary:<\/b> 95,000
Name:<\/b> Miths \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/div>3. Numeric Variation Algorithms: Generating Realistic Data<\/h3><\/div>By applying numeric or date variation algorithms, it is possible to create a fictitious dataset derived from the original numerical information. By defining a meaningful variation range (e.g. \u00b110%), you can produce results close to reality while making it impossible to retrieve the original dataset.<\/p>\n
Example :<\/b><\/p>\n<\/div>Original dataset<\/h4><\/div>Name:<\/b> Brown \u2013 Salary: <\/b>95,000
Name:<\/b> Smith \u2013 Salary:<\/b> 125,000<\/p>\n<\/div><\/div><\/div><\/i><\/div><\/div>
With substitution algorithms, specific data fields are replaced by other values. The resulting information still appears real but allows anonymization and protection of individuals\u2019 identities in the dataset.<\/p>\n
Example:<\/b><\/p>\n<\/div> Name:<\/b> Brown \u2013 Salary:<\/b> 95,000 Name:<\/b> Green \u2013 Salary: <\/b>95,000 This algorithm randomly rearranges characters within each column, making it very difficult to reconstruct the original information.<\/p>\n Example :<\/b><\/p>\n<\/div> Name:<\/b> Brown \u2013 Salary:<\/b> 95,000 Name:<\/b> Worbn \u2013 Salary:<\/b> 95,000 By applying numeric or date variation algorithms, it is possible to create a fictitious dataset derived from the original numerical information. By defining a meaningful variation range (e.g. \u00b110%), you can produce results close to reality while making it impossible to retrieve the original dataset.<\/p>\n Example :<\/b><\/p>\n<\/div> Name:<\/b> Brown \u2013 Salary: <\/b>95,000Original dataset<\/h4><\/div>
Name:<\/b> Smith \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div>Anonymized dataset<\/h4><\/div>
Name:<\/b> Jones \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/div>2. Randomization Algorithms: Shuffling Data<\/h3><\/div>
Original dataset<\/h4><\/div>
Name:<\/b> Smith \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div>Anonymized dataset<\/h4><\/div>
Name:<\/b> Miths \u2013 Salary: <\/b>125,000<\/p>\n<\/div><\/div><\/div><\/div>3. Numeric Variation Algorithms: Generating Realistic Data<\/h3><\/div>
Original dataset<\/h4><\/div>
Name:<\/b> Smith \u2013 Salary:<\/b> 125,000<\/p>\n<\/div><\/div><\/div>