fable-client 0.5.0

HTTP-based client for interacting with the FABLE service for privacy-preserving record linkage with Bloom filters.

FABLE Client

This package contains a HTTP-based client for working with the server provided by the PPRL service which is part of the FABLE (Federated Anonymized Bloom filter Linkage Engine) ecosystem. It also contains a command-line application which uses the library to process CSV files.

Installation

pip install fable-client

Weight estimation requires additional packages which are not shipped by default. To add them, install this package using the following command.

pip install fable-client[faker]

Library methods

The library exposes functions for entity pre-processing, masking and bit vector matching. They follow the data model that is also used by the FABLE PPRL service, which is exposed through the FABLE model package.

Entity transformation

import fable_client
from fable_model import (
    EntityTransformRequest,
    TransformConfig,
    EmptyValueHandling,
    AttributeValueEntity,
    GlobalTransformerConfig,
    NormalizationTransformer,
)

client = fable_client.FableClient(base_url="http://localhost:8080")

response = client.transform(
    EntityTransformRequest(
        config=TransformConfig(empty_value=EmptyValueHandling.error),
        entities=[AttributeValueEntity(id="001", attributes={"first_name": "Müller", "last_name": "Ludenscheidt"})],
        global_transformers=GlobalTransformerConfig(before=[NormalizationTransformer()]),
    )
)

print(response.entities)
# => [AttributeValueEntity(id='001', attributes={'first_name': 'muller', 'last_name': 'ludenscheidt'})]

Entity masking

import fable_client
from fable_model import (
    EntityMaskRequest,
    MaskConfig,
    HashConfig,
    HashFunction,
    HashAlgorithm,
    RandomHash,
    CLKFilter,
    AttributeValueEntity,
)

client = fable_client.FableClient(base_url="http://localhost:8080")

response = client.mask(
    EntityMaskRequest(
        config=MaskConfig(
            token_size=2,
            hash=HashConfig(
                function=HashFunction(algorithms=[HashAlgorithm.sha1], key="s3cr3t_k3y"), strategy=RandomHash()
            ),
            filter=CLKFilter(hash_values=5, filter_size=256),
        ),
        entities=[AttributeValueEntity(id="001", attributes={"first_name": "muller", "last_name": "ludenscheidt"})],
    )
)

print(response.entities)
# => [BitVectorEntity(id='001', value='SKkgqBHBCJJCANICEKSpWMAUBYCQEMLuZgEQGBKRC8A=')]

Bit vector matching

import fable_client
from fable_model import VectorMatchRequest, MatchConfig, SimilarityMeasure, BitVectorEntity

client = fable_client.FableClient(base_url="http://localhost:8080")

response = client.match(
    VectorMatchRequest(
        config=MatchConfig(measure=SimilarityMeasure.jaccard, threshold=0.8),
        domain=[BitVectorEntity(id="001", value="SKkgqBHBCJJCANICEKSpWMAUBYCQEMLuZgEQGBKRC8A=")],
        range=[
            BitVectorEntity(id="100", value="UKkgqBHBDJJCANICELSpWMAUBYCMEMLrZgEQGBKRC7A="),
            BitVectorEntity(id="101", value="H5DN45iUeEjrjbHZrzHb3AyQk9O4IgxcpENKKzEKRLE="),
        ],
    )
)

print(response.matches)
# => [Match(domain=BitVectorEntity(id='001', value='SKkgqBHBCJJCANICEKSpWMAUBYCQEMLuZgEQGBKRC8A='), range=BitVectorEntity(id='100', value='UKkgqBHBDJJCANICELSpWMAUBYCMEMLrZgEQGBKRC7A='), similarity=0.8536585365853658)]

Attribute weight estimation

import fable_client
from fable_model import (
    AttributeValueEntity,
    BaseTransformRequest,
    TransformConfig,
    EmptyValueHandling,
    GlobalTransformerConfig,
    NormalizationTransformer,
)

client = fable_client.FableClient(base_url="http://localhost:8080")

stats = fable_client.estimate.compute_attribute_stats(
    client,
    [
        AttributeValueEntity(id="001", attributes={"given_name": "Max", "last_name": "Mustermann", "gender": "m"}),
        AttributeValueEntity(id="002", attributes={"given_name": "Maria", "last_name": "Musterfrau", "gender": "f"}),
    ],
    BaseTransformRequest(
        config=TransformConfig(empty_value=EmptyValueHandling.skip),
        global_transformers=GlobalTransformerConfig(before=[NormalizationTransformer()]),
    ),
)

print(stats)
# => {'given_name': {'average_tokens': 5.0, 'ngram_entropy': 2.9219280948873623}, 'last_name': {'average_tokens': 11.0, 'ngram_entropy': 3.913977073182751}, 'gender': {'average_tokens': 2.0, 'ngram_entropy': 2.0}}

Command line interface

The fable command exposes all the library's functions and adapts them to work with CSV files. Running fable --help provides an overview of the command options.

$ fable --help
Usage: fable [OPTIONS] COMMAND [ARGS]...

  HTTP client for performing PPRL based on Bloom filters.

Options:
  --base-url TEXT                 base URL to HTTP-based PPRL service
  -b, --batch-size INTEGER RANGE  amount of bit vectors to match at a time  [x>=1]
  --timeout-secs INTEGER RANGE    seconds until a request times out  [x>=1]
  --delimiter TEXT                column delimiter for CSV files
  --encoding TEXT                 character encoding for files
  --help                          Show this message and exit.

Commands:
  estimate   Estimate attribute weights based on randomly generated data.
  mask       Mask a CSV file with entities.
  match      Match bit vectors from CSV files against each other.
  transform  Perform pre-processing on a CSV file with entities

The fable command works on two basic types of CSV files that follow a simple structure. Entity files are CSV files that contain a column with a unique identifier and arbitrary additional columns which contain values for certain attributes that identify an entity. Each row is representative of a single entity.

id,first_name,last_name,date_of_birth,gender
001,Natalie,Sampson,1956-12-16,female
002,Eric,Lynch,1910-01-11,female
003,Pam,Vaughn,1983-10-05,male
004,David,Jackson,2006-01-27,male
005,Rachel,Dyer,1904-02-02,female

Bit vector files contain an ID column and a value column which contains a representative bit vector. These bit vectors are generally generated by masking a record from an entity file.

id,value
001,0Dr8t+kE5ltI+xdM85fwx0QLrTIgvFN35/0YvODNdOE0AaUHPphikXYy4LlArE4UqfjPs+wKtT233R7lBzSp5mwkCjTzA1tl0N7s+sFeKyIrOiGk0gNIYvA=
002,QMEIkE9TN1Quv0K0QAIk1RZD3qF7nQh0IyOYqVDf8IQkyaLGcFjiLHsEgBpU8CRSCuATbWpjEwGi3dilizySQy4miGiJolilYmwKysjseq+IFsAU3T1IRjA=
003,BqFoNZhrAVBq9SV1wBK0dUZLHDM9hCBoO4XdKCzvasSUELQeAB8+DV5tAhDl5KCSJfDCB6JG4WSoCFbozXqBYSUMqEQJE0JwhpRK6oLOcRRoGwGESDBMZwA=
004,8C9KItMTwtz4oXQvo8G0t1bTnwspnghmJwyqqcL2RIHASb4XJHAqybMCXQBm5mq6h/kdxGbblxBjhy79jRUcI60haqZhNsst0n7OUAxM/UoZVumIilRIbCA=
005,CFk4I0sKwnRoiTEOQASy1QZfHCGB1GBgYQDcZwDDtIkGGLOmLRhrQyOSlQDUDoYTbvaBRVqbkRnqmYQbDTEGlG+2y60FMmBEKtxsr0I4I00oMpuoXAsDWmA=

Pre-processing is done with the fable transform command. It requires a base transform request file, an entity file and an output file to write the pre-processed entities to. Attribute and global transformer configurations can be provided, but at least one must be specified.

In this example, a global normalization transformer which is executed before all other attribute-specific transformers is defined. Date time reformatting is applied to the "date of birth" column in the input file.

request.json

{
  "config": {
    "empty_value": "skip"
  },
  "attribute_transformers": [
    {
      "attribute_name": "date_of_birth",
      "transformers": [
        {
          "name": "date_time",
          "input_format": "%Y-%m-%d",
          "output_format": "%Y%m%d"
        }
      ]
    }
  ],
  "global_transformers": {
    "before": [
      {
        "name": "normalization"
      }
    ]
  }
}

$ fable transform ./request.json ./input.csv ./output.csv  
Transforming entities  [####################################]  100%

output.csv

id,first_name,last_name,date_of_birth,gender
001,natalie,sampson,19561216,female
002,eric,lynch,19100111,female
003,pam,vaughn,19831005,male
004,david,jackson,20060127,male
005,rachel,dyer,19040202,female

Masking is done with fable mask and its subcommands. It requires a base mask request file, an entity file and an output file to write the masked entities to.

request.json

{
  "config": {
    "token_size": 2,
    "hash": {
      "function": {
        "algorithms": ["sha256"],
        "key": "s3cr3t_k3y",
        "strategy": {
          "name": "random_hash"
        }
      }
    },
    "prepend_attribute_name": true,
    "filter": {
      "type": "clk",
      "filter_size": 512,
      "hash_values": 5,
      "padding": "_",
      "hardeners": [
        {
          "name": "permute",
          "seed": 727
        },
        {
          "name": "rehash",
          "window_size": 16,
          "window_step": 8,
          "samples": 2
        }
      ]
    }
  }
}

input.csv

id,first_name,last_name,date_of_birth,gender
001,natalie,sampson,19561216,female
002,eric,lynch,19100111,female
003,pam,vaughn,19831005,male
004,david,jackson,20060127,male
005,rachel,dyer,19040202,female

$ fable mask ./request.json ./input.csv ./output.csv
Masking entities  [####################################]  100%

output.csv

id,value
001,wAWgITvQ1/VACpRYC2EKrfCkWziyEhmyKwi5sMsFrAQVoIBygTQScPRoIIAto0AwS0ihlcAIFAcQRwccY5IOmQ==
002,cFCwQIABQ+TgSSdlGM/z54BEUgmYhA1GKtCxQAKAXFIWiPAFIQYaFArgM61pUAAeATwBlBEOEw4Oowe0rbcMGw==
003,IgK16AAISCRoCuVAb1UBZYBBhGgxSEkKeMkTUCKAx4IAsNGJBS4ShgBAGIapBIQWJLiBFEEKAIWAGYS8ZZGMKw==
004,ZlBkyoYIEWmeaxbPDNng5JjHACkCAJwjlBCJQBJ4ZBSyOAukACUahOAFQ20oNwTQEDRA005+VUUfsUQcKCGNxg==
005,cUekQFQkI7TpTcRwmcNDoodRRBshlSEiAUjBQiMlxBLTmODMJICmDmxgUqYKonQEMFD58QsogRQFIgYUwJDOHA==

Matching is done with the fable match command. It allows the matching of multiple bit vector input files at once. If more than two files are provided, the command will pick out pairs of files and matches their contents against one another.

In this example, the bit vectors of two files are matched against each other. The Jaccard index is used as a similarity measure and a match threshold of 70% is applied.

request.json

{
  "config": {
    "measures": "jaccard",
    "thresholds": 0.7
  }
}

domain.csv

id,value
001,wAWgITvQ1/VACpRYC2EKrfCkWziyEhmyKwi5sMsFrAQVoIBygTQScPRoIIAto0AwS0ihlcAIFAcQRwccY5IOmQ==
002,cFCwQIABQ+TgSSdlGM/z54BEUgmYhA1GKtCxQAKAXFIWiPAFIQYaFArgM61pUAAeATwBlBEOEw4Oowe0rbcMGw==
003,IgK16AAISCRoCuVAb1UBZYBBhGgxSEkKeMkTUCKAx4IAsNGJBS4ShgBAGIapBIQWJLiBFEEKAIWAGYS8ZZGMKw==
004,ZlBkyoYIEWmeaxbPDNng5JjHACkCAJwjlBCJQBJ4ZBSyOAukACUahOAFQ20oNwTQEDRA005+VUUfsUQcKCGNxg==
005,cUekQFQkI7TpTcRwmcNDoodRRBshlSEiAUjBQiMlxBLTmODMJICmDmxgUqYKonQEMFD58QsogRQFIgYUwJDOHA==

range.csv

id,value
101,kUSyxIgtIDSAB7ZYDkFQRZpFoMkCjCCCbDTWAUJTRAAEBpspBX4PNUZKi1AIVCABAjg6EAoKuwVleeUYgRBYoQ==
102,IAA0YE4MGexIiYdEjwNzoOKmIA4CEHEiKQASYFPhxQTQlPAAgYW3AWBYmQJ8YMoaAj0ZkoOrFyUmFo52TDcIKw==
103,BFAwREkkQbTdzddgDHFWgMRJMyxAMW+jq2ASICMBtIEr+YDCBRUgxEDIsQpciO4mAK3h2cIbXFQCMlaVpJPZIQ==
104,wBWgITvQ2/VACpRYC2EKrfCkWxiyEhmyKwi5sMsFrBQVoIBygTQScPRoIIAto0AwS0ihldAIFAcQRwccY5IOmQ==
105,QCCwIKQAED5AjaZYmodDcZAEBKkIxgAiDfEUoDKEdgEAEJAMAwcfQEbQkaQ4ANAABqiUscAKPQZEMJxRhTGIGQ==

$ fable match request.json domain.csv range.csv output.csv
Matching bit vectors from domain.csv and range.csv  [####################################]  100%

output.csv

domain_id,domain_file,range_id,range_file,similarities,aggregated_similarity
001,domain.csv,104,range.csv,[0.9690721649484536],

Weight estimation is done with the fable estimate command. It generates random data based off of user specification and computes estimates for attribute weights. Data can be generated using Faker.

faker.json

{
  "seed": 727,
  "count": 5000,
  "locale": ["de_DE"],
  "generators": [
    {"function_name": "first_name_nonbinary", "attribute_name": "given_name"},
    {"function_name": "last_name", "attribute_name": "last_name"},
    {"function_name": "random_element", "attribute_name": "gender", "args": {"elements": ["m", "f"]}},
    {"function_name": "street_name", "attribute_name": "street_name"},
    {"function_name": "city", "attribute_name": "municipality"},
    {"function_name": "postcode", "attribute_name": "postcode"}
  ]
}

$ fable estimate faker faker.json faker-output.json

faker-output.json

[
  {
    "attribute_name": "given_name",
    "weight": 7.657958943890718,
    "average_token_count": 7.5686
  },
  {
    "attribute_name": "last_name",
    "weight": 7.444573503220938,
    "average_token_count": 7.5204
  },
  {
    "attribute_name": "gender",
    "weight": 1.9999971146079947,
    "average_token_count": 2.0
  },
  {
    "attribute_name": "street_name",
    "weight": 7.605565770282046,
    "average_token_count": 16.2188
  },
  {
    "attribute_name": "municipality",
    "weight": 7.659422921807241,
    "average_token_count": 9.952
  },
  {
    "attribute_name": "postcode",
    "weight": 6.7812429085107,
    "average_token_count": 5.9464
  }
]

Configuring pytest

In order to run integration tests, the FABLE PPRL service is needed. The first option is to spin up the service independently and direct pytest to it. Alternatively, pytest can start a Docker test container for the duration of the test run. The following table shows all available configuration options. These variables can be defined in .env or .env.test.

Environment variable	Description	Default
PYTEST_PPRL_BASE_URL1)	Base URL for the FABLE PPRL service
PYTEST_PPRL_SERVICE_VERSION	Tag of the FABLE PPRL service image that will run inside the test container
PYTEST_PRRL_SERVICE_PORT	Port that will be exposed by the test container	8080

¹⁾ If defined, pytest will not spin up a test container.

License

MIT.