pprl-client 0.4.0

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

This package contains a small HTTP-based library for working with the server provided by the PPRL service. It also contains a command-line application which uses the library to process CSV files.

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

$ pip install pprl_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 PPRL service, which is exposed through the PPRL model package.

Entity transformation

import pprl_client
from pprl_model import (
    EntityTransformRequest,
    TransformConfig,
    EmptyValueHandling,
    AttributeValueEntity,
    GlobalTransformerConfig,
    NormalizationTransformer,
)

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

Entity masking

import pprl_client
from pprl_model import (
    EntityMaskRequest,
    MaskConfig,
    HashConfig,
    HashFunction,
    HashAlgorithm,
    RandomHash,
    CLKFilter,
    AttributeValueEntity,
)

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

Bit vector matching

import pprl_client
from pprl_model import VectorMatchRequest, MatchConfig, SimilarityMeasure, BitVectorEntity

client = pprl_client.PPRLClient(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 pprl_client
from pprl_model import (
    AttributeValueEntity,
    BaseTransformRequest,
    TransformConfig,
    EmptyValueHandling,
    GlobalTransformerConfig,
    NormalizationTransformer,
)

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

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

$ pprl --help
Usage: pprl [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 pprl 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 pprl 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"
      }
    ]
  }
}

$ pprl 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 pprl 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

$ pprl 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 pprl 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": {
    "measure": "jaccard",
    "threshold": 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==

$ pprl 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,similarity
001,domain.csv,104,range.csv,0.9690721649484536

Weight estimation is done with the pprl 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"}
  ]
}

$ pprl 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
  }
]

License

MIT.