Various Masking Strategies

The extension provides functions to implement 8 main anonymization strategies:

Depending on your data, you may need to use different strategies on different columns :

  • For names and other 'direct identifiers' , Faking is often useful
  • Shuffling is convenient for foreign keys
  • Adding Noise is interesting for numeric values and dates
  • Partial Scrambling is perfect for email address and phone numbers
  • etc.


First of all, the fastest and safest way to anonymize a data is to destroy it :-)

In many cases, the best approach to hide the content of a column is to replace all the values with a single static value.

For instance, you can replace a entire column by the word 'CONFIDENTIAL' like this:

  ON COLUMN users.address

Adding Noise

This is also called Variance. The idea is to "shift" dates and numeric values. For example, by applying a +/- 10% variance to a salary column, the dataset will remain meaningful.

  • anon.noise(original_value,ratio) where original_value can be an integer, a bigint or a double precision. If the ratio is 0.33, the return value will be the original value randomly shifted with a ratio of +/- 33%

  • anon.dnoise(original_value, interval) where original_value can be a date, a timestamp, or a time. If interval = '2 days', the return value will be the original value randomly shifted by +/- 2 days

WARNING : The noise() masking functions are vulnerable to a form of repeat attack, especially with Dynamic Masking. A masked user can guess an original value by requesting its masked value multiple times and then simply use the AVG() function to get a close approximation. (See demo/noise_reduction_attack.sql for more details). In a nutshell, these functions are best fitted for Anonymous Dumps and Static Masking. They should be avoided when using Dynamic Masking.


The extension provides a large choice of functions to generate purely random data :

Basic Random values

  • anon.random_date() returns a date
  • anon.random_string(n) returns a TEXT value containing n letters
  • anon.random_zip() returns a 5-digit code
  • anon.random_phone(p) returns a 8-digit phone with p as a prefix
  • anon.random_hash(seed) returns a hash of a random string for a given seed

Random between

To pick any value inside between two bounds:

  • anon.random_date_between(d1,d2) returns a date between d1 and d2
  • anon.random_int_between(i1,i2) returns an integer between i1 and i2
  • anon.random_bigint_between(b1,b2) returns a bigint between b1 and b2

NOTE: With these functions, the lower and upper bounds are included. For instance anon.random_int_between(1,3) returns either 1, 2 or 3.

For more advanced interval descriptions, check out the [Random in Range] section.

Random in Array

The random_in function returns an element a given array

For example:

  • anon.random_in(ARRAY[1,2,3]) returns an int between 1 and 3
  • anon.random_in(ARRAY['red','green','blue']) returns a text

Random in Enum

This is one especially useful when working with ENUM types!

  • anon.random_in_enum(variable_of_an_enum_type) returns any val
CREATE TYPE card AS ENUM ('visa', 'mastercard', ‘amex’);

SELECT anon.random_in_enum(NULL::CARD);

CREATE TABLE customer (
  id INT,
  credit_card CARD

IS 'MASKED WITH FUNCTION anon.random_in_enum(creditcard)'

Random in Range

RANGE types are a powerfull way to describe an interval of values, where can define inclusive or excluvive bounds:

There a function for each subtype of range:

  • anon.random_in_int4range('[5,6)') returns an INT of value 5
  • anon.random_in_int8range('(6,7]') returns a BIGINT of value 7
  • `anon.random_in_numrange('[0.1,0.9]') returns a NUMERIC between 0.1 and 0.9
  • anon.random_in_daterange('[2001-01-01, 2001-12-31)') returns a date in 2001
  • anon.random_in_tsrange('[2022-10-01,2022-10-31]') returns a TIMESTAMP in october 2022
  • anon.random_in_tstzrange('[2022-10-01,2022-10-31]') returns a TIMESTAMP WITH TIMEZONE in october 2022

NOTE: It is not possible to get a random value from a RANGE with an infinite bound. For example anon.random_in_int4range('[2022,)') returns NULL.


The idea of Faking is to replace sensitive data with random-but-plausible values. The goal is to avoid any identification from the data record while remaining suitable for testing, data analysis and data processing.

In order to use the faking functions, you have to init() the extension in your database first:

SELECT anon.init();

The init() function will import a default dataset of random data (iban, names, cities, etc.).

This dataset is in English and very small ( 1000 values for each category ). If you want to use localized data or load a specific dataset, please read the Custom Fake Data section.

Once the fake data is loaded, you have access to these faking functions:

  • anon.fake_address() returns a complete post address
  • anon.fake_city() returns an existing city
  • anon.fake_country() returns a country
  • anon.fake_company() returns a generic company name
  • anon.fake_email() returns a valid email address
  • anon.fake_first_name() returns a generic first name
  • anon.fake_iban() returns a valid IBAN
  • anon.fake_last_name() returns a generic last name
  • anon.fake_postcode() returns a valid zipcode
  • anon.fake_siret() returns a valid SIRET

For TEXT and VARCHAR columns, you can use the classic Lorem Ipsum generator:

  • anon.lorem_ipsum() returns 5 paragraphs
  • anon.lorem_ipsum(2) returns 2 paragraphs
  • anon.lorem_ipsum( paragraphs := 4 ) returns 4 paragraphs
  • anon.lorem_ipsum( words := 20 ) returns 20 words
  • anon.lorem_ipsum( characters := 7 ) returns 7 characters
  • anon.lorem_ipsum( characters := anon.length(table.column) ) returns the same amount of characters as the original string

Advanced Faking

Generating fake data is a complex topic. The functions provided here are limited to basic use case. For more advanced faking methods, in particular if you are looking for localized fake data, take a look at PostgreSQL Faker, an extension based upon the well-known Faker python library.

This extension provides an advanced faking engine with localisation support.

For example:

SELECT faker.faker('de_DE');
SELECT faker.first_name_female();


Pseudonymization is similar to Faking in the sense that it generates realistic values. The main difference is that the pseudonymization is deterministic : the functions always will return the same fake value based on a seed and an optional salt.

In order to use the faking functions, you have to init() the extension in your database first:

SELECT anon.init();

Once the fake data is loaded you have access to 10 pseudo functions:

  • anon.pseudo_first_name(seed,salt) returns a generic first name
  • anon.pseudo_last_name(seed,salt) returns a generic last name
  • anon.pseudo_email(seed,salt) returns a valid email address
  • anon.pseudo_city(seed,salt) returns an existing city
  • anon.pseudo_country(seed,salt) returns a country
  • anon.pseudo_company(seed,salt) returns a generic company name
  • anon.pseudo_iban(seed,salt) returns a valid IBAN
  • anon.pseudo_siret(seed,salt) returns a valid SIRET

The second argument (salt) is optional. You can call each function with only the seed like this anon.pseudo_city('bob'). The salt is here to increase complexity and avoid dictionary and brute force attacks (see warning below). If a specific salt is not given, the value of the anon.salt GUC parameter is used instead (see the Generic Hashing section for more details).

The seed can be any information related to the subject. For instance, we can consistently generate the same fake email address for a given person by using her login as the seed :

  ON COLUMN users.emailaddress
  IS 'MASKED WITH FUNCTION anon.pseudo_email(users.login) ';

NOTE : You may want to produce unique values using a pseudonymization function. For instance, if you want to mask an email column that is declared as UNIQUE. In this case, you will need to initialize the extension with a fake dataset that is way bigger than the numbers of rows of the table. Otherwise you may see some "collisions" happening, i.e. two different original values producing the same pseudo value.

WARNING : Pseudonymization is often confused with anonymization but in fact they serve 2 different purposes : pseudonymization is a way to protect the personal information but the pseudonymized data is still "linked" to the real data. The GDPR makes it very clear that personal data which has undergone pseudonymization is still related to a person. (see GDPR Recital 26)

Generic hashing

In theory, hashing is not a valid anonymization technique, however in practice it is sometimes necessary to generate a determinist hash of the original data.

For instance, when a pair of primary key / foreign key is a "natural key", it may contain actual information ( like a customer number containing a birth date or something similar).

Hashing such columns allows to keep referential integrity intact even for relatively unusual source data. Therefore, the

  • anon.digest(value,salt,algorithm) lets you choose a salt, and a hash algorithm from a pre-defined list

  • anon.hash(value) will return a text hash of the value using a secret salt (defined by the anon.salt parameter) and hash algorithm (defined by the anon.algorithm parameter). The default value of anon.algorithm is sha256 and possible values are: md5, sha1, sha224, sha256, sha384 or sha512. The default value of anon.salt is an empty string. You can modify these values with:

sql ALTER DATABASE foo SET anon.salt TO 'xsfnjefnjsnfjsnf'; ALTER DATABASE foo SET anon.algorithm TO 'sha384';

Keep in mind that hashing is a form a Pseudonymization. This means that the data can be "de-anonymized" using the hashed value and the masking function. If an attacker gets access to these 2 elements, he or she could re-identify some persons using brute force or dictionary attacks. Therefore, the salt and the algorithm used to hash the data must be protected with the same level of security that the original dataset.

In a nutshell, we recommend that you use the anon.hash() function rather than anon.digest() because the salt will not appear clearly in the masking rule.

Furthermore: in practice the hash function will return a long string of character like this:

SELECT anon.hash('bob');

For some columns, this may be too long and you may have to cut some parts the hash in order to fit into the column. For instance, if you have a foreign key based on a phone number and the column is a VARCHAR(12) you can transform the data like this:

SECURITY LABEL FOR anon ON COLUMN people.phone_number
IS 'MASKED WITH FUNCTION anon.left(anon.hash(phone_number),12)';

SECURITY LABEL FOR anon ON COLUMN call_history.fk_phone_number
IS 'MASKED WITH FUNCTION anon.left(anon.hash(fk_phone_number),12)';

Of course, cutting the hash value to 12 characters will increase the risk of "collision" (2 different values having the same fake hash). In such case, it's up to you to evaluate this risk.

Partial Scrambling

Partial scrambling leaves out some part of the data. For instance : a credit card number can be replaced by '40XX XXXX XXXX XX96'.

2 functions are available:

  • anon.partial('abcdefgh',1,'xxxx',3) will return 'axxxxfgh';
  • anon.partial_email('') will become 'da**@gm****.com'

Conditional Masking

In some situations, you may want to apply a masking filter only for some value or for a limited number of lines in the table.

For instance, if you want to "preserve NULL values", i.e. masking only the lines that contains a value, you can use the anon.ternary function, which works like a CASE WHEN x THEN y ELSE z statement :

  IS 'MASKED WITH FUNCTION anon.ternary(score IS NULL,

You may also want to exclude some lines within the table. Like keeping the password of some users so that they still may be able to connect to a testing deployment of your application:

SECURITY LABEL FOR anon ON COLUMN account.password
  IS 'MASKED WITH FUNCTION anon.ternary( id > 1000, NULL::TEXT, password)';

WARNING : Conditional masking may create a partially deterministic "connection" between the original data and the masked data. And that connection can be used to retrieve personal information from the masked data. For instance, if NULL values are preserved for a "deceased_date" column, it will reveal which persons are still actually alive... In a nutshell: conditional masking may often produce a dataset that is not fully anonymized and therefore would still technically contain personal information.


Generalization is the principle of replacing the original value by a range containing this value. For instance, instead of saying 'Paul is 42 years old', you would say 'Paul is between 40 and 50 years old'.

The generalization functions are a data type transformation. Therefore it is not possible to use them with the dynamic masking engine. However they are useful to create anonymized views. See example below.

Let's imagine a table containing health information:

SELECT * FROM patient;
 id |   name   |  zipcode |   birth    |    disease
  1 | Alice    |    47678 | 1979-12-29 | Heart Disease
  2 | Bob      |    47678 | 1959-03-22 | Heart Disease
  3 | Caroline |    47678 | 1988-07-22 | Heart Disease
  4 | David    |    47905 | 1997-03-04 | Flu
  5 | Eleanor  |    47909 | 1999-12-15 | Heart Disease
  6 | Frank    |    47906 | 1968-07-04 | Cancer
  7 | Geri     |    47605 | 1977-10-30 | Heart Disease
  8 | Harry    |    47673 | 1978-06-13 | Cancer
  9 | Ingrid   |    47607 | 1991-12-12 | Cancer

We can build a view upon this table to suppress some columns ( SSN and name ) and generalize the zipcode and the birth date like this:

CREATE VIEW anonymized_patient AS
    'REDACTED' AS lastname,
    anon.generalize_int4range(zipcode,100) AS zipcode,
    anon.generalize_tsrange(birth,'decade') AS birth
FROM patients;

The anonymized table now looks like that:

SELECT * FROM anonymized_patient;
 lastname |   zipcode     |           birth             |    disease
 REDACTED | [47600,47700) | ["1970-01-01","1980-01-01") | Heart Disease
 REDACTED | [47600,47700) | ["1950-01-01","1960-01-01") | Heart Disease
 REDACTED | [47600,47700) | ["1980-01-01","1990-01-01") | Heart Disease
 REDACTED | [47900,48000) | ["1990-01-01","2000-01-01") | Flu
 REDACTED | [47900,48000) | ["1990-01-01","2000-01-01") | Heart Disease
 REDACTED | [47900,48000) | ["1960-01-01","1970-01-01") | Cancer
 REDACTED | [47600,47700) | ["1970-01-01","1980-01-01") | Heart Disease
 REDACTED | [47600,47700) | ["1970-01-01","1980-01-01") | Cancer
 REDACTED | [47600,47700) | ["1990-01-01","2000-01-01") | Cancer

The generalized values are still useful for statistics because they remain true, but they are less accurate, and therefore reduce the risk of re-identification.

PostgreSQL offers several RANGE data types which are perfect for dates and numeric values.

For numeric values, 3 functions are available:

  • generalize_int4range(value, step)
  • generalize_int8range(value, step)
  • generalize_numrange(value, step)

...where value is the data that will be generalized, and step is the size of each range.

Using pg_catalog functions

Since version 1.3, the pg_catalog schema is not trusted by default. This is a security measure designed to prevent users from using sophisticated functions in masking rules (such as pg_catalog.query_to_xml, pg_catalog.ts_stat or the system administration functions ) that should not be used as masking functions.

However, the extension provides bindings to some useful and safe functions from the pg_catalog schema for your convenience:

  • anon.concat(TEXT,TEXT)
  • anon.concat(TEXT,TEXT, TEXT)
  • anon.date_part(TEXT,TIMESTAMP)
  • anon.date_part(TEXT,INTERVAL)
  • anon.date_subtract(TIMESTAMP WITH TIME ZONE, INTERVAL )
  • anon.date_trunc(TEXT,TIMESTAMP)
  • anon.date_trunc(TEXT,INTERVAL)
  • anon.left(TEXT,INTEGER)
  • anon.length(TEXT)
  • anon.lower(TEXT)
  • anon.make_date(INT,INT,INT )
  • anon.make_time(INT,INT,DOUBLE PRECISION)
  • anon.md5(TEXT)
  • anon.random()
  • anon.replace(TEXT,TEXT,TEXT)
  • anon.regexp_replace(TEXT,TEXT,TEXT)
  • anon.regexp_replace(TEXT,TEXT,TEXT,TEXT)
  • anon.right(TEXT,INTEGER)
  • anon.substr(TEXT,INTEGER)
  • anon.substr(TEXT,INTEGER,INTEGER)
  • anon.upper(TEXT)

If you need more bindings, you can either

  • Write your own mapping function in a trusted schema (see below)
  • Set the pg_catalog schema as TRUSTED (not recommended)
  • open an issue

Write your own Masks !

You can also use your own function as a mask. The function must either be destructive (like Partial Scrambling) or insert some randomness in the dataset (like Faking).

Especially for complex data types, you may have to write your own function. This will be a common use case if you have to hide certain parts of a JSON field.

For example:

CREATE TABLE company (
  business_name TEXT,
  info JSONB

The info field contains unstructured data like this:

SELECT jsonb_pretty(info) FROM company WHERE business_name = 'Soylent Green';
     "employees": [
             "lastName": "Doe",
             "firstName": "John"
             "lastName": "Smith",
             "firstName": "Anna"
             "lastName": "Jones",
             "firstName": "Peter"
(1 row)

Using the PostgreSQL JSON functions and operators, you can walk through the keys and replace the sensitive values as needed.

CREATE SCHEMA custom_masks;

-- This step requires superuser privilege

CREATE FUNCTION custom_masks.remove_last_name(j JSONB)
AS $func$
    'employees' ,
      jsonb_set(e ,'{lastName}', to_jsonb(anon.fake_last_name()))
FROM jsonb_array_elements( j->'employees') e

Then check that the function is working correctly:

SELECT custom_masks.remove_last_name(info) FROM company;

When that's ok you can declare this function as the mask of the info field:

IS 'MASKED WITH FUNCTION custom_masks.remove_last_name(info)';

And try it out !

# SELECT anonymize_table('company');
# SELECT jsonb_pretty(info) FROM company WHERE business_name = 'Soylent Green';
     "employees": [                 +
         {                          +
             "lastName": "Prawdzik",+
             "firstName": "John"    +
         },                         +
         {                          +
             "lastName": "Baltazor",+
             "firstName": "Anna"    +
         },                         +
         {                          +
             "lastName": "Taylan",  +
             "firstName": "Peter"   +
         }                          +
     ]                              +
(1 row)

This is just a quick and dirty example. As you can see, manipulating a sophisticated JSON structure with SQL is possible, but it can be tricky at first! There are multiple ways of walking through the keys and updating values. You will probably have to try different approaches, depending on your real JSON data and the performance you want to reach.