Dapla Toolbelt Pseudo

Pseudonymize, repseudonymize and depseudonymize data on Dapla.

Features

Other examples can also be viewed through notebook files for pseudo and depseudo

Pseudonymize

from dapla_pseudo import Pseudonymize
import polars as pl

file_path="data/personer.csv"
dtypes = {"fnr": pl.Utf8, "fornavn": pl.Utf8, "etternavn": pl.Utf8, "kjonn": pl.Categorical, "fodselsdato": pl.Utf8}
df = pl.read_csv(file_path, dtypes=dtypes) # Create DataFrame from file

# Example: Single field default encryption (DAEAD)
result_df = (
    Pseudonymize.from_polars(df)                   # Specify what dataframe to use
    .on_fields("fornavn")                          # Select the field to pseudonymize
    .with_default_encryption()                     # Select the pseudonymization algorithm to apply
    .run()                                         # Apply pseudonymization to the selected field
    .to_polars()                                   # Get the result as a polars dataframe
)

# Example: Multiple fields default encryption (DAEAD)
result_df = (
    Pseudonymize.from_polars(df)                   # Specify what dataframe to use
    .on_fields("fornavn", "etternavn")             # Select multiple fields to pseudonymize
    .with_default_encryption()                     # Select the pseudonymization algorithm to apply
    .run()                                         # Apply pseudonymization to the selected fields
    .to_polars()                                   # Get the result as a polars dataframe
)

# Example: Single field sid mapping and pseudonymization (FPE)
result_df = (
    Pseudonymize.from_polars(df)                   # Specify what dataframe to use
    .on_fields("fnr")                              # Select the field to pseudonymize
    .with_stable_id()                              # Map the selected field to stable id
    .run()                                         # Apply pseudonymization to the selected fields
    .to_polars()                                   # Get the result as a polars dataframe
)

The default encryption algorithm is DAEAD (Deterministic Authenticated Encryption with Associated Data). However, if the field is a valid Norwegian personal identification number (fnr, dnr), the recommended way to pseudonymize is to use the function with_stable_id() to convert the identification number to a stable ID (SID) prior to pseudonymization. In that case, the pseudonymization algorithm is FPE (Format Preserving Encryption).

Note that you may also use a Pandas DataFrame as an input or output, by exchanging from_polars with from_pandas and to_polars with to_pandas. However, Pandas is much less performant, so take special care especially if your dataset is large.

Example:

# Example: Single field default encryption (DAEAD)
df_pandas = (
    Pseudonymize.from_pandas(df)                   # Specify what dataframe to use
    .on_fields("fornavn")                          # Select the field to pseudonymize
    .with_default_encryption()                     # Select the pseudonymization algorithm to apply
    .run()                                         # Apply pseudonymization to the selected field
    .to_pandas()                                   # Get the result as a polars dataframe
)

Validate SID mapping

from dapla_pseudo import Validator
import polars as pl

file_path="data/personer.csv"
dtypes = {"fnr": pl.Utf8, "fornavn": pl.Utf8, "etternavn": pl.Utf8, "kjonn": pl.Categorical, "fodselsdato": pl.Utf8}
df = pl.read_polars(file_path, dtypes=dtypes)

result = (
    Validator.from_polars(df)                   # Specify what dataframe to use
    .on_field("fnr")                            # Select the field to validate
    .validate_map_to_stable_id()                # Validate that all the field values can be mapped to a SID
)
# The resulting dataframe contains the field values that didn't have a corresponding SID
result.to_polars()

A sid_snapshot_date can also be specified to validate that the field values can be mapped to a SID at a specific date:

from dapla_pseudo import Validator
from dapla_pseudo.utils import convert_to_date
import polars as pl

file_path="data/personer.csv"
dtypes = {"fnr": pl.Utf8, "fornavn": pl.Utf8, "etternavn": pl.Utf8, "kjonn": pl.Categorical, "fodselsdato": pl.Utf8}

df = pl.read_csv(file_path, dtypes=dtypes)

result = (
    Validator.from_polars(df)
    .on_field("fnr")
    .validate_map_to_stable_id(
        sid_snapshot_date=convert_to_date("2023-08-29")
    )
)
# Show metadata about the validation (e.g. which version of the SID catalog was used)
result.metadata
# Show the field values that didn't have a corresponding SID
result.to_polars()

Advanced usage

Pseudonymize

Read from file systems

from dapla_pseudo import Pseudonymize
from dapla import AuthClient


file_path="data/personer.csv"

options = {
    "dtypes": {"fnr": pl.Utf8, "fornavn": pl.Utf8, "etternavn": pl.Utf8, "kjonn": pl.Categorical, "fodselsdato": pl.Utf8}
}


# Example: Read DataFrame from file
result_df = (
    Pseudonymize.from_file(file_path)   # Read the data from file
    .on_fields("fornavn", "etternavn")  # Select multiple fields to pseudonymize
    .with_default_encryption()          # Select the pseudonymization algorithm to apply
    .run()                              # Apply pseudonymization to the selected fields
    .to_polars(**options)               # Get the result as a Pandas DataFrame
)

# Example: Read dataframe from GCS bucket
options = {
    "dtypes": {"fnr": pl.Utf8, "fornavn": pl.Utf8, "etternavn": pl.Utf8, "kjonn": pl.Categorical, "fodselsdato": pl.Utf8}
}

gcs_file_path = "gs://ssb-staging-dapla-felles-data-delt/felles/pseudo-examples/andeby_personer.csv"

result_df = (
    Pseudonymize.from_file(gcs_file_path)  # Read DataFrame from GCS
    .on_fields("fornavn", "etternavn")     # Select multiple fields to pseudonymize
    .with_default_encryption()             # Select the pseudonymization algorithm to apply
    .run()                                 # Apply pseudonymization to the selected fields
    .to_polars(**options)                  # Get the result as a polars dataframe
)

Pseudonymize using custom keys/keysets

from dapla_pseudo import pseudonymize

# Pseudonymize fields in a local file using the default key:
df = (
    Pseudonymize.from_polars(df)                            # Specify what dataframe to use
    .on_fields("fornavn")                                   # Select the field to pseudonymize
    .with_default_encryption()                              # Select the pseudonymization algorithm to apply
    .run()                                         # Apply pseudonymization to the selected field
    .to_polars()                                            # Get the result as a polars dataframe
)

# Pseudonymize fields in a local file, explicitly denoting the key to use:
df = (
    Pseudonymize.from_polars(df)                            # Specify what dataframe to use
    .on_fields("fornavn")                                   # Select the field to pseudonymize
    .with_default_encryption(custom_key="ssb-common-key-2") # Select the pseudonymization algorithm to apply
    .run()                                         # Apply pseudonymization to the selected field
    .to_polars()                                            # Get the result as a polars dataframe
)
pseudonymize(file_path="./data/personer.json", fields=["fnr", "fornavn"], key="ssb-common-key-1")

# Pseudonymize a local file using a custom keyset:
import json
custom_keyset = PseudoKeyset(
    encrypted_keyset="CiQAp91NBhLdknX3j9jF6vwhdyURaqcT9/M/iczV7fLn...8XYFKwxiwMtCzDT6QGzCCCM=",
    keyset_info={
        "primaryKeyId": 1234567890,
        "keyInfo": [
            {
                "typeUrl": "type.googleapis.com/google.crypto.tink.AesSivKey",
                "status": "ENABLED",
                "keyId": 1234567890,
                "outputPrefixType": "TINK",
            }
        ],
    },
    kek_uri="gcp-kms://projects/some-project-id/locations/europe-north1/keyRings/some-keyring/cryptoKeys/some-kek-1",
)

df = (
    Pseudonymize.from_polars(df)
    .on_fields("fornavn")
    .with_default_encryption(custom_key="1234567890") # Note that the custom key has to be the same as "primaryKeyId" in the custom keyset
    .run(custom_keyset=custom_keyset)
    .to_polars()
)

Depseudonymize

The "Depseudonymize" functions are almost exactly the same as when pseudonymizing. The only difference being the lack of a "with_stable_id()"-function. This is to say, that you cannot map from Stable ID back to FNR as of Jan 2023.

from dapla_pseudo import Depseudonymize
import polars as pl

file_path="data/personer_pseudonymized.csv"
dtypes = {"fnr": pl.Utf8, "fornavn": pl.Utf8, "etternavn": pl.Utf8, "kjonn": pl.Categorical, "fodselsdato": pl.Utf8}
df = pl.read_csv(file_path, dtypes=dtypes) # Create DataFrame from file

# Example: Single field default encryption (DAEAD)
result_df = (
    Depseudonymize.from_polars(df)                 # Specify what dataframe to use
    .on_fields("fornavn")                          # Select the field to depseudonymize
    .with_default_encryption()                     # Select the depseudonymization algorithm to apply
    .run()                                         # Apply depseudonymization to the selected field
    .to_polars()                                   # Get the result as a polars dataframe
)

# Example: Multiple fields default encryption (DAEAD)
result_df = (
    Depseudonymize.from_polars(df)                 # Specify what dataframe to use
    .on_fields("fornavn", "etternavn")             # Select multiple fields to depseudonymize
    .with_default_encryption()                     # Select the depseudonymization algorithm to apply
    .run()                                         # Apply depseudonymization to the selected fields
    .to_polars()                                   # Get the result as a polars dataframe
)

Repseudonymize

## TODO

Note that depseudonymization requires elevated access privileges.

Requirements

• Python >= 3.10
• Dependencies can be found in pyproject.toml

Installation

You can install Dapla Toolbelt Pseudo via pip from PyPI:

pip install dapla-toolbelt-pseudo

Usage

Please see the Reference Guide for details.

Contributing

Contributions are very welcome. To learn more, see the Contributor Guide.

License

Distributed under the terms of the MIT license, Dapla Toolbelt Pseudo is free and open source software.

Issues

If you encounter any problems, please file an issue along with a detailed description.

Credits

This project was generated from Statistics Norway's SSB PyPI Template.