links

Ideas and Resources

Videos / Presentations

• French: https://www.youtube.com/watch?v=KGSlp4UygdU
• English: https://www.youtube.com/watch?v=niIIFL4s-L8
• Chinese: https://www.youtube.com/watch?v=n9atI31FcSM

Similar technologies

Here’s a list of open-source projects with similar goals

• database anonymizer An anonymizing ETL for MySQL and PostgreSQL (Anonymous Export)

• greenmask Anonymous dump utility written in Golang (Anonymous Export)

• pganonymize A commandline tool for anonymizing PostgreSQL databases (Anonymous Export)

• pgantomizer Anonymous dumps based on masking rules written in a YAML file (Anonymous Export)

• pgsodium and postgresql-anonymizer Pseudonymous Access To Encrypted Table (Dynamic Masking)

• pg_diffix PostgreSQL extension implementing differential privacy (Dynamic Masking)

• pg_anonymize PostgreSQL extension implementing dynamic data anonymization (Dynamic Masking)

• pg-anonymizer Dump anonymized PostgreSQL database with a NodeJS CLI (Anonymous Export)

• pg-mask Simple data masking for PostgreSQL (Static Masking)

• pgEdge Anonymizer An anonymizer tool for replacing PII and similar data in dev/test databases (Static Masking)

Similar Implementations

• Dynamic Data Masking With MS SQL Server

• Citus : Using search_path and views to hide columns for reporting with Postgres

• MariaDB : Masking with maxscale

GDPR

• Ultimate Guide to Data Anonymization

• UK ICO Anonymisation Code of Practice

• L. Sweeney, Simple Demographics Often Identify People Uniquely, 2000

• How Google anonymizes data

• IAPP’s Guide To Anonymisation

Concepts

• Differential_Privacy

• K-Anonymity

Academic Research

• L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002, pp. 557-570. https://epic.org/wp-content/uploads/privacy/reidentification/Sweeney_Article.pdf

• A. Narayanan and V. Shmatikov, “Robust de-anonymization of large sparse datasets,” in 29th IEEE Symposium on Security and Privacy, 2008, pp. 111–125. https://www.cs.cornell.edu/~shmat/shmat_oak08netflix.pdf