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                            CERT-Renater

                Note d'Information No. 2026/VULN610
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DATE                : 10/06/2026

HARDWARE PLATFORM(S): /

OPERATING SYSTEM(S): Systems running PostgreSQL Anonymizer versions 
                                    prior to 3.1.

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https://www.postgresql.org/about/news/postgresql-anonymizer-31-introducing-local-differential-privacy-3311/
_____________________________________________________________________


PostgreSQL Anonymizer 3.1 : Introducing Local Differential Privacy
Posted on 2026-06-09 by Dalibo


Eymoutiers, France, May 27th, 2026

Dalibo is pleased to announce PostgreSQL Anonymizer 3.1 introducing
innovative data masking techniques to protect your data !

Enhanced Privacy Protection for Your Data

PostgreSQL Anonymizer is an extension that hides or replaces
personally identifiable information (PII) or commercially sensitive
data from a PostgreSQL database.

The extension offers 6 different masking strategies:

    Dynamic Masking - Real-time data protection
    Static Masking - Permanent data transformation
    Replica Masking - Anonymized logical replication
    Backup Masking - Privacy-protected database exports
    Masking Views - Controlled data visibility
    Masking Data Wrappers - Extended protection across systems

Each strategy is complemented by an enhanced suite of Masking
Functions, including advanced techniques such as: Substitution,
Randomization, Faking, Pseudonymization, Partial Scrambling,
Shuffling, Noise Addition and Generalization.

The extension can be installed with Debian and RPM packages, an
Ansible role, a Docker image, etc. You can use it on most major
DBaaS providers including : Alibaba Cloud, Crunchy Bridge,
Google Cloud SQL, IBM Cloud, Microsoft Azure Database, Neon,
Yandex It is also available on some Postgres forks such as
EDB Advanced Postgres, Greenplum and Yugabyte.

See the INSTALL section of the documentation for more details!


Local Differential Privacy (LDP)

Local Differential Privacy is a stronger approach to adding noise.
Unlike the regular noise functions, LDP provides a formal
mathematical guarantee: given the output, an observer cannot
determine the original value with high confidence, no matter
what auxiliary information they have. The strength of this
guarantee is controlled by a parameter called epsilon -- a
smaller epsilon means stronger privacy but less accuracy.

This is particularly useful for survey data and categorical
values (e.g. ratings, age brackets, answer choices) where you
want to collect aggregate statistics while protecting
individual responses.

Currently LDP is achieved using the Generalized Randomized
Response Mechanism (GRRM). Additional mechanisms may be
introduced in the near future.


Important Security Update

Version 3.1 includes fixes for a critical vulnerability
allowing users to gain superuser privileges under certains
circumstances. The risk is very high on PostgreSQL 14 and
on instances upgrades from PostgreSQL 14 and earlier.

All users should upgrade the extension to version 3.1 as
soon as possible.

If a quick upgrade is not possible, the workaround below
can mitigate the risk:

CREATE OR REPLACE FUNCTION anon.k_anonymity(relid regclass)
RETURNS INTEGER AS $$ SELECT NULL::INTEGER $$ LANGUAGE SQL;

For more details see issue 640 (CVE-2026-9617).


Acknowledgments

This release includes code, bugfixes, documentation, code
reviews and ideas from Adem Bencheikh Lehocine, Benoit Lobréau,
Buut, and other contributors.

The Local Differential Privacy features are part of a
larger research project named DIFPRIPOS aiming at
integrating differential privacy mechanisms into
PostgreSQL. This project is financed by ANR, the French
National Research Agency. Many thanks to
Jean-François Couchot and Cedric Eichler for coordination
and oversight.

We would also like to thanks the people at Efluid who
helped us with their ideas, comments and testing.

And also special thanks to the PGRX team for their
amazing work!


Join our community to improve data privacy!

PostgreSQL Anonymizer is part of the [Dalibo Labs]
initiative. It is mainly developed by [Damien Clochard].

This is an open project, contributions are welcome. We
need your feedback and ideas! Let us know what you think
of this tool, how it fits your needs and what features
are missing.

If you want to help, you can find a list of Junior Jobs.

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