Describes the types of filters available in Philter.
Each filter is capable of identifying and redacting a specific type of sensitive information. For example, there is a filter for phone numbers, a filter for US social security numbers, and a filter for person's names. You can enable any combination of these filters based on the types of sensitive information you need to redact. This section of the documentation describes the available filters and how to enable and configure each.

Predefined Filters

Philter can identify many predefined types of sensitive information. Each type, or filter, can be enabled or disabled separately from the other types in a filter profile.

Person's Names

Philter uses several methods to identify person's names.
Identifies common first names
Identifies common surnames
Identifies full names using natural language processing analysis

Other Filters

Identifies ages such as 3.5 years old
Identifies Bitcoin addresses such as 127NVqnjf8gB9BFAW2dnQeM6wqmy1gbGtv
Identifies common cities
Identifies common counties
Identifies VISA, American Express, MasterCard, and Discover credit card numbers.
Identifies dates in many formats such as May 22, 1999
Identifies driver's license numbers for all 50 US states
Identifies email addresses
Identifies common hospital names and their abbreviations
Identifies international bank account numbers
Identifies IPv4 and IPv6 addresses
Identifies network MAC addresses
Identifies US passport numbers
Identifies phone numbers and phone number extensions
Identifies sections in text denoted by
Identifies US SSNs and TINs
Identifies US state names and abbreviations
Identifies UPS, FedEx, and USPS tracking numbers
Identifies URLs
Identifies vehicle identification numbers
Zip Codes
Identifies US zip codes

Custom Filter Types of Sensitive Information

In addition to the predefined types of sensitive information listed in the table above, you can also define your own types of sensitive information. Through custom identifiers and dictionaries, Philter can identify many other types of information that may be sensitive in your use-case. For example, if you have patient identifiers that follow a pattern of AA-00000 you can define a custom identifier for this sensitive information.
Philter can be configured to look identify sensitive information based on custom dictionaries. When a term in the dictionary is found in the text, Philter will treat the term as sensitive information and apply the given replacement strategy.
Custom dictionaries support fuzziness to accommodate for misspellings. The replacement strategy for a custom dictionary has a sensitivityLevel that controls the amount of allowed fuzziness.
Identifies sensitive information based on dictionary values.
Identifies custom alphanumeric identifiers that may be used for medical record numbers, patient identifiers, account number, or other specific identifier.