Data masking and anonymization:
understanding the different algorithms
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.
According to the CNIL (French Data Protection Authority), anonymization aims to make it impossible to identify an individual from their data.
In this article, we explore the main data masking algorithms used to protect information while maintaining data consistency and usefulness.
Types of Data Masking Algorithms
1. Substitution Algorithms: Preserving a Realistic Appearance
With substitution algorithms, specific data fields are replaced by other values. The resulting information still appears real but allows anonymization and protection of individuals’ identities in the dataset.
Example:
Original dataset
Name: Brown – Salary: 95,000
Name: Smith – Salary: 125,000
Anonymized dataset
Name: Green – Salary: 95,000
Name: Jones – Salary: 125,000
2. Randomization Algorithms: Shuffling Data
This algorithm randomly rearranges characters within each column, making it very difficult to reconstruct the original information.
Example :
Original dataset
Name: Brown – Salary: 95,000
Name: Smith – Salary: 125,000
Anonymized dataset
Name: Worbn – Salary: 95,000
Name: Miths – Salary: 125,000
3. Numeric Variation Algorithms: Generating Realistic Data
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. ±10%), you can produce results close to reality while making it impossible to retrieve the original dataset.
Example :
Original dataset
Name: Brown – Salary: 95,000
Name: Smith – Salary: 125,000
Anonymized dataset
Name: Brown – Salary: 102,600
Name: Smith – Salary: 112,500
4. Redaction Algorithms: Artificially Replacing Data
To make a dataset completely anonymous, a redaction algorithm can replace all real data with a constant or random string. This is essentially a substitution algorithm where the resulting information no longer appears authentic.
Example :
Original dataset
Name: Brown – Salary: 95,000
Name: Smith – Salary: 125,000
Anonymized dataset
Name: xxxxx – Salary: 95,000
Name: xxxxx – Salary: 125,000
5. Masking Algorithms: Keeping the Database Usable
Similar to the redaction algorithm, the masking algorithm performs a partial redaction, keeping some parts of the data visible during anonymization.
Example :
Original dataset
Name: Brown – Salary: 95,000
Name: Smith – Salary: 125,000
Anonymized dataset
Name: Bxxxx – Salary: 95,000
Name: Sxxxx – Salary: 125,000
6. Custom Algorithms: Meeting Specific Business Needs
Sometimes, the standard algorithms are not sufficient or do not meet a specific business requirement. In these cases, custom algorithms can be implemented. Companies may, for example, request that certain fields be swapped between rows to anonymize data.
Example :
Original dataset
Name: Brown – Salary: 95,000
Name: Smith – Salary: 125,000
Anonymized dataset
Name: Brown – Salary: 125,000
Name: Smith – Salary: 95,000
Conclusion: Protect Your Data Without Losing Its Value
Data masking and anonymization algorithms allow organizations to secure sensitive data effectively while preserving its business value.
Each method offers unique benefits and fits specific business contexts. The key is to choose the right approach according to your confidentiality, compliance, and performance needs.