Anonymization and pseudonymization of data

Behind the Mask

Conclusions

Data anonymization and pseudonymization are essential data protection techniques that are particularly crucial for SMEs. They enable a balance between data security and usability and ensure compliance with legal requirements, in particular the GDPR.

With the use of techniques such as generalization, suppression, perturbation, and differential privacy, along with tools such as Apache Spark and ARX, companies can effectively protect personal data. At the same time, pseudonymization methods such as tokenization and encryption provide robust security measures. Future technologic developments, including artificial intelligence and blockchain, will continue to change the data protection landscape.

Infos

Apache Spark: https://spark.apache.org
ARX: https://arx.deidentifier.org/anonymization-tool/
sdcMicro: https://sdctools.github.io/sdcMicro/articles/sdcMicro.html
Format-preserving encryption: https://en.wikipedia.org/wiki/Format-preserving_encryption
PostgreSQL Anonymizer 1.0: https://www.postgresql.org/about/news/postgresql-anonymizer-10-privacy-by-design-for-postgres-2452/
MongoDB data masking: https://www.mongodb.com/resources/products/capabilities/working-with-sensitive-data-while-keeping-it-secure-a-guide-to-data-masking
Cassandra DDM: https://cassandra.apache.org/doc/latest/cassandra/developing/cql/dynamic-data-masking.html
pgcrypto: https://www.postgresql.org/docs/current/pgcrypto.html

The Author

Daniel Richey is deputy editor-in-chief at IT-Administrator magazine, where he covers the topics of IT security, infrastructure, and law. Before becoming an editor 20 years ago, Daniel studied economics and then taught economics and IT as a freelance lecturer at the Stuttgart University of Cooperative Education.

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