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]
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.
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'})]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=')]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)]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}}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
}
]MIT.