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Address data represents physical locations around the world, for example an office or personal mailing address. Addresses are usually structured, requiring several columns.
Addresses are also a type of PII data. A key consideration is that you do not want the precise address locations of your dataset to leak such as the exact street address and building number. However, you may want to create realistic, broader regions such as the city, postal code or country.
Create random, realistic regions from anywhere in the world. When you do this, the combinations of all your address columns will make sense when put together. For example a city, state, country combination such as Boston, Massachusetts, USA.
You can also use the the Contextual Anonymization technique on addresses. Preserve the real regions from your data while anonymizing precise locations only, such as the street address and building number.
An address comprises of multiple columns, each with specific concept such as a city.
Below are the supported sdtypes. You can supply one or more of these in combination to form an address.
A 2-character ISO country code such as "US" or "BE". For a full list of ISO country codes, view the ISO guide.
The full name of the broader regions in a country. In some countries, these are called states. The general name of the term is administrative unit. For example "Massachusetts" or "California".
The shortened abbreviation for the administrative unit. These are determined by the country. For example Massachusetts is "MA" and California is "CA".
The full name of the city, for example "Boston" or "San Francisco".
The internationally-recognized, 5-digit postal service code such as "02116" or "94103".
The building number and street such as "229 Berkeley St" or "77 Massachusetts Ave".
An optional, second line of the address that further specifies the unit or apartment number. For example "Apt #4" or "Building 204".