FABLE Client

This package contains HTTP-based clients for working with the servers provided by the PPRL service and the Broker service which are 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 clients

The library exposes the PPRLClient class for entity pre-processing, masking, bit vector matching and weight estimation. Furthermore, there is the BrokerClient class for handling match sessions, accepting bit vectors from different clients, querying matching tasks for workers in the background and providing results to these clients. They follow the data models that are also used by the FABLE services themselves, which are exposed through the FABLE model package.

PPRL Client

The following demonstrates the four main tasks of the PPRLClient: transformation, masking, matching and weight estimation.

Entity transformation

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

client = fable_client.PPRLClient(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(
    json.dumps(
        [entity.model_dump() for entity in response.entities],
        indent=2,
    )
)

[
  {
    "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,
)
import json

client = fable_client.PPRLClient(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(
    json.dumps(
        [entity.model_dump() for entity in response.entities],
        indent=2,
    )
)

[
  {
    "id": "001",
    "value": "SKkgqBHBCJJCANICEKSpWMAUBYCQEMLuZgEQGBKRC8A="
  }
]

Bit vector matching

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

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

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

print(
    json.dumps(
        [match.model_dump() for match in response.matches],
        indent=2,
    )
)

[
  {
    "domain": {
      "id": "001",
      "value": "SKkgqBHBCJJCANICEKSpWMAUBYCQEMLuZgEQGBKRC8A="
    },
    "range": {
      "id": "100",
      "value": "UKkgqBHBDJJCANICELSpWMAUBYCMEMLrZgEQGBKRC7A="
    },
    "similarities": [
      0.8536585365853658
    ],
    "aggregated_similarity": null
  }
]

Attribute weight estimation

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

client = fable_client.PPRLClient(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(json.dumps(stats, indent=2))

{
  "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
  }
}

Broker Client

The following demonstrates the five main tasks of the BrokerClient: creating sessions, refreshing sessions, deleting sessions, submitting vectors from different clients and fetching matching results. For further explanation read the documentation of the Broker service repository.

Creating a session

from datetime import datetime
from fable_client import BrokerClient
from fable_model import SessionCreationRequest, MatchConfig, SimilarityMeasure, SimilarityAggregator

client = BrokerClient(base_url="http://localhost:8081")

r = client.create_session(
    SessionCreationRequest(
        session="my-session-id",
        match_config=MatchConfig(
            measures=[SimilarityMeasure.jaccard, SimilarityMeasure.dice],
            thresholds=[0.9],
            aggregator=SimilarityAggregator.avg,
        ),
    ),
)

print(r.session)
print(datetime.fromtimestamp(r.expires_at))
print(r.token)

my-session-id
2026-07-10 11:33:01
7beb3d4d3527b26984b867453f24c02a

Refreshing a session

from datetime import datetime
from fable_client import BrokerClient
from fable_model import SessionUpdateRequest

client = BrokerClient(base_url="http://localhost:8081")

r = client.refresh_session(
    SessionUpdateRequest(
        session="my-session-id",
        token="7beb3d4d3527b26984b867453f24c02a",
    ),
)

print(r.session)
print(datetime.fromtimestamp(r.expires_at))

my-session-id
2026-07-10 12:03:01

Deleting a session

from fable_client import BrokerClient
from fable_model import SessionDeletionRequest

client = BrokerClient(base_url="http://localhost:8081")

client.delete_session(
    SessionDeletionRequest(
        session="my-session-id",
        token="7beb3d4d3527b26984b867453f24c02a",
    ),
)

Submitting vectors for different clients

from fable_client import BrokerClient
from fable_model import ClientSubmissionRequest, MetaBitVectorEntity, BitVectorMetadata

client = BrokerClient(base_url="http://localhost:8081")

client.submit(
    ClientSubmissionRequest(
        session="my-session-id",
        client="my-client-id",
        vectors=[
            MetaBitVectorEntity(
                id="001",
                value="CE9stxXqVmVQkHiZAZfE9w==",
                metadata=[
                    BitVectorMetadata(
                        name="count",
                        value="10",
                    ),
                ],
            ),
        ],
    ),
)

client.submit(
    ClientSubmissionRequest(
        session="my-session-id",
        client="my-client-id-2",
        vectors=[
            MetaBitVectorEntity(
                id="001",
                value="DE/st1XqViVQkHiZCJfE9w==",
                metadata=[
                    BitVectorMetadata(
                        name="count",
                        value="5",
                    ),
                ],
            ),
        ],
    ),
)

Fetching results

from fable_client import BrokerClient
from fable_model import ClientResultRequest

client = BrokerClient(base_url="http://localhost:8081")

r = client.result(
    ClientResultRequest(
        session="my-session-id",
        client="my-client-id",
    ),
)

match = r.matches[0]

print(match.vector.id)
print(match.similarities)
print(match.aggregated_similarity)
print(match.reference_metadata)

True
1
001
[0.90625, 0.9508196721311475]
0.9285348360655737
[BitVectorMetadata(name='count', value='5')]

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 and Broker services are 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.

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

License

MIT.