Eymoutiers, France, November 6, 2019
Postgresql Anonymizer
is an extension that hides or replaces personally
identifiable information (PII) or commercially sensitive data from a PostgreSQL
database.
The extension supports 3 different anonymization strategies: Dynamic Masking, In-Place Anonymization and Anonymous Dumps. It also offers a large choice of Masking Functions: Substitution, Randomization, Faking, Partial Scrambling, Shuffling, Noise Addition and Generalization.
The idea of generalization is to replace data with a broader, less accurate value. For instance, instead of saying "Bob is 28 years old", you can say "Bob is between 20 and 30 years old". This is interesting for analytics because the data remains true while avoiding the risk of re-identification.
PostgreSQL can handle generalization very easily with the RANGE data types, a very powerful way to store and manipulate a set of values contained between a lower and an upper bound.
Here's a basic table containing medical data:
SELECT * FROM patient;
ssn | firstname | zipcode | birth | disease
-------------+-----------+---------+------------+---------------
253-51-6170 | Alice | 47012 | 1989-12-29 | Heart Disease
091-20-0543 | Bob | 42678 | 1979-03-22 | Allergy
565-94-1926 | Caroline | 42678 | 1971-07-22 | Heart Disease
510-56-7882 | Eleanor | 47909 | 1989-12-15 | Acne
We want the anonymized data to remain true because it will be used for statistics. We can build a view upon this table to remove useless columns and generalize the indirect identifiers (zipcode and birthday):
CREATE MATERIALIZED VIEW generalized_patient AS
SELECT
'REDACTED'::TEXT AS firstname,
anon.generalize_int4range(zipcode,1000) AS zipcode,
anon.generalize_daterange(birth,'decade') AS birth,
disease
FROM patient;
This will give us a less accurate view of the data:
SELECT * FROM generalized_patient;
firstname | zipcode | birth | disease
-----------+---------------+-------------------------+---------------
REDACTED | [47000,48000) | [1980-01-01,1990-01-01) | Heart Disease
REDACTED | [42000,43000) | [1970-01-01,1980-01-01) | Allergy
REDACTED | [42000,43000) | [1970-01-01,1980-01-01) | Heart Disease
REDACTED | [47000,48000) | [1980-01-01,1990-01-01) | Acne
k-anonymity is an industry-standard term used to describe a property of an
anonymized dataset. The k-anonymity principle states that within a
given dataset, any anonymized individual cannot be distinguished from at
least k-1
other individuals. In other words, k-anonymity might be described
as a "hiding in the crowd" guarantee. A low value of k
indicates there's a risk
of re-identification using linkage with other data sources.
You can evaluate the k-anonymity factor of a table in 2 steps :
1/ First defined the columns that are [indirect idenfiers] ( also known as "quasi identifers") like this:
SECURITY LABEL FOR anon ON COLUMN generalized_patient.zipcode
IS 'INDIRECT IDENTIFIER';
SECURITY LABEL FOR anon ON COLUMN generalized_patient.birth
IS 'INDIRECT IDENTIFIER';
2/ Once the indirect identifiers are declared :
SELECT anon.k_anonymity('generalized_patient')
In the example above, the k-anonymity factor of the generalized_patient
materialized view is 2
.
For TEXT and VARCHAR columns, you can now use the classic Lorem Ipsum generator:
anon.lorem_ipsum()
returns 5 paragraphsanon.lorem_ipsum(2)
returns 2 paragraphsanon.lorem_ipsum( paragraphs := 4 )
returns 4 paragraphsanon.lorem_ipsum( words := 20 )
returns 20 wordsanon.lorem_ipsum( characters := 7 )
returns 7 charactersThis extension is officially supported on PostgreSQL 9.6 and later.
On Red Hat / CentOS systems, you can install it from the official PostgreSQL RPM repository:
$ yum install postgresql_anonymizer12
Then add 'anon' in the shared_preload_libraries
parameter of your
postgresql.conf
file. And restart your instance.
For other system, check out the install documentation :
https://postgresql-anonymizer.readthedocs.io/en/latest/INSTALL/
WARNING: The project is at an early stage of development and should be used carefully.
This release includes code and ideas from Travis Miller, Jan Birk and Olleg Samoylov. Many thanks to them !
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
here:
https://gitlab.com/dalibo/postgresql_anonymizer/issues?label_name%5B%5D=Junior+Jobs