Unique, deterministic study ids, psuedonyms, and pseudodobs for all!


As a Python library:

>>> from diana.utils.guid import GUIDMint
>>> GUIDMint().get_sham_id( name="MERCK^DEREK^L", age=30 )
  'BirthDate':, 11, 20),
  'Name': ['VANWASSENHOVE', 'XAVIER', 'N'],
  'TimeOffset': datetime.timedelta(-47, 82822)

From diana-cli:

$ diana-cli guid "MERCK^DEREK^L" --age 30
Generating GUID
WARNING:GUIDMint:Creating non-reproducible GUID using current date
{'birth_date': '19881120',
 'time_offset': '-47 days, 23:00:22'}

Or from the diana-REST api:

$ curl -X GET "http://localhost:8080/v1.0/guid?name=MERCK%5EDEREK%5EL&age=30&sex=U"
  "birth_date": "19881120",
  "time_offset": "-47 days, 23:00:22"


The GUID mint generates a unique and reproducibly generated tag against any consistent set of object-specific variables:

  • name (or any string)
  • gender ({m, f, u})
  • birth date (or age + reference date)

Global Unique ID

Generation Algorithm:

  1. Given name, gender, and dob parameters. Depending on the available data, name may be a patient name, an MRN, or a subject ID, or any unique combination of those elements. If dob is unavailable, an age parameter and a reference_date may be substituted. If no reference date is provided the algorithm defaults to today and the GUID will be unreproducible.
  2. A unique key is generated based on the alphabetically sorted elements of name, dob, and gender.
  3. The sha256 hash of the key is computed and the result is encoded into base32
  4. If the first three characters are not alphabetic, the value is rehashed until it is (for pseudonym generation)

Pseudonym Generation

It is often useful to replace the subject name with something more natural than a GUID.
Any string beginning with at least 3 (capitalized) alphabetic characters can be used to reproducibly generate a “John Doe” style placeholder name in DICOM patient name format (last^first^middle)`1 <>`__. This is very useful for alphabetizing subject name lists similarly to their ID while still allowing for anonymized data sets to be referenced according to memorable names.

Generation Algorithm:

  1. Given a guid and gender (M,F,U) (optional, defaults to U)
  2. Using the guid as a random seed, a gender-appropriate first name and gender-neutral family name is selected from a uniform distribution taken from the US census
  3. The result is returned in DICOM patient name format.

The default name map can be easily replaced to match your fancy (Shakespearean names, astronauts, children book authors). With slight modification, a DICOM patient name with up to 5 elements could be generated (i.e., in last^first^middle^prefix^suffix format).

Approximate Date-of-Birth

As with pseudonyms, it can be useful to maintain a valid date-of-birth (dob) in de-identified metadata. Using a GUID as a seed, any dob can be mapped to a random nearby date for a nearly-age-preserving anonymization strategy. This is useful for keeping an approximate patient age available in a data browser.

Generation Algorithm:

  1. Given a GUID and a dob parameter
  2. Using the guid as a random seed, a random integer between -90 and +90 is selected
  3. The original dob + the random delta in days is returned

Study-Time Offset

In order to keep study date-times in the correct order, a similar algorithm is used to generate a days and seconds time offset that will keep the study at roughly the same time of day (within an hour) while offseting the study date up to +/-90 days.