Tutorial for Python API

For this tutorial we are going to process a data set for private linkage with clkhash using the Python API.

The Python package recordlinkage has a tutorial linking data sets in the clear, we will try duplicate that in a privacy preserving setting.

First install the dependencies we will need:

# NBVAL_IGNORE_OUTPUT
!pip install -U clkhash anonlink recordlinkage pandas

# NBVAL_IGNORE_OUTPUT
import io
import itertools
import pandas as pd

import clkhash
from clkhash import clk
from clkhash.field_formats import *
from clkhash.schema import Schema
from clkhash.comparators import NgramComparison
from clkhash.serialization import serialize_bitarray

from recordlinkage.datasets import load_febrl4

Data Exploration

First load the dataset, and preview the first few rows.

dfA, dfB = load_febrl4()

dfA.head()

	given_name	surname	street_number	address_1	address_2	suburb	postcode	state	date_of_birth	soc_sec_id
rec_id
rec-1070-org	michaela	neumann	8	stanley street	miami	winston hills	4223	nsw	19151111	5304218
rec-1016-org	courtney	painter	12	pinkerton circuit	bega flats	richlands	4560	vic	19161214	4066625
rec-4405-org	charles	green	38	salkauskas crescent	kela	dapto	4566	nsw	19480930	4365168
rec-1288-org	vanessa	parr	905	macquoid place	broadbridge manor	south grafton	2135	sa	19951119	9239102
rec-3585-org	mikayla	malloney	37	randwick road	avalind	hoppers crossing	4552	vic	19860208	7207688

For this linkage we will not use the social security id column.

dfA.columns

Index(['given_name', 'surname', 'street_number', 'address_1', 'address_2',
       'suburb', 'postcode', 'state', 'date_of_birth', 'soc_sec_id'],
      dtype='object')

In this tutorial we will use StringIO buffers instead of files. Let’s dump the data from the pandas dataframe into a csv:

a_csv = io.StringIO()
dfA.to_csv(a_csv)

Linkage Schema Definition

A hashing schema instructs clkhash how to treat each feature when encoding a CLK.

The linkage schema below details a 1024 bit encoding using equally weighted features. Most features are encoding using bigrams although the postcode and date of birth use unigrams. The schema specifies to ignore the columns 'rec_id' and 'soc_sec_id'.

A detailed description of the linkage schema can be found in the documentation.

fields = [
    Ignore('rec_id'),
    StringSpec('given_name', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(300))),
    StringSpec('surname', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(300))),
    IntegerSpec('street_number', FieldHashingProperties(comparator=NgramComparison(1, True), strategy=BitsPerFeatureStrategy(300), missing_value=MissingValueSpec(sentinel=''))),
    StringSpec('address_1', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(300))),
    StringSpec('address_2', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(300))),
    StringSpec('suburb', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(300))),
    IntegerSpec('postcode', FieldHashingProperties(comparator=NgramComparison(1, True), strategy=BitsPerFeatureStrategy(300))),
    StringSpec('state', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(300))),
    IntegerSpec('date_of_birth', FieldHashingProperties(comparator=NgramComparison(1, True), strategy=BitsPerFeatureStrategy(300), missing_value=MissingValueSpec(sentinel=''))),
    Ignore('soc_sec_id')
]

schema = Schema(fields, 1024)

Encode the data

We can now encode our PII data from the CSV file using our defined schema. We must provide a secret to this command - this secret has to be used by both parties hashing data. For this toy example we will use the secret "secret", for real data, make sure that the key contains enough entropy, as knowledge of this secret is sufficient to reconstruct the PII information from a CLK!

Also, do not share this secret with anyone, except the other participating party.

secret = 'secret'

a_csv.seek(0)
hashed_data_a = clk.generate_clk_from_csv(a_csv, secret, schema)

generating CLKs: 100%|██████████| 5.00k/5.00k [00:03<00:00, 1.39kclk/s, mean=944, std=14.4]

Inspect the output

clkhash has encoded the PII, creating a Cryptographic Longterm Key for each entity. The output of generate_clk_from_csv shows that the mean popcount is quite high, more than 900 out of 1024 bits are set on average which can affect accuracy.

We can control the popcount by adjusting the strategy. There are currently two different strategies implemented in the library:

• BitsPerToken: each token of a feature’s value is inserted into the encoding bits_per_token times. Increasing bits_per_token will give the corresponding feature more importance in comparisons, decreasing bits_per_token will de-emphasise columns which are less suitable for linkage (e.g. information that changes frequently). The BitsPerToken strategy is set with the strategy=BitsPerTokenStrategy(bits_per_token=30) argument for a feature’s FieldHashingProperties.

• BitsPerFeature: In this strategy we always insert a fixed number of bits into the CLK for a feature, irrespective of the number of tokens. This strategy is set with the strategy=BitsPerFeatureStrategy(bits_per_feature=100) argument for a feature’s FieldHashingProperties.

In this example, we will reduce the value of bits_per_feature for address related columns.

fields = [
    Ignore('rec_id'),
    StringSpec('given_name', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(200))),
    StringSpec('surname', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(200))),
    IntegerSpec('street_number', FieldHashingProperties(comparator=NgramComparison(1, True), strategy=BitsPerFeatureStrategy(100), missing_value=MissingValueSpec(sentinel=''))),
    StringSpec('address_1', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(100))),
    StringSpec('address_2', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(100))),
    StringSpec('suburb', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(100))),
    IntegerSpec('postcode', FieldHashingProperties(comparator=NgramComparison(1, True), strategy=BitsPerFeatureStrategy(100))),
    StringSpec('state', FieldHashingProperties(comparator=NgramComparison(2), strategy=BitsPerFeatureStrategy(100))),
    IntegerSpec('date_of_birth', FieldHashingProperties(comparator=NgramComparison(1, True), strategy=BitsPerFeatureStrategy(200), missing_value=MissingValueSpec(sentinel=''))),
    Ignore('soc_sec_id')
]

schema = Schema(fields, 1024)
a_csv.seek(0)
clks_a = clk.generate_clk_from_csv(a_csv, secret, schema)

generating CLKs: 100%|██████████| 5.00k/5.00k [00:02<00:00, 2.20kclk/s, mean=696, std=22.7]

Each CLK is represented by a bitarray but can be serialized in a compact, JSON friendly base64 format:

print("original:")
print(clks_a[0])
print("serialized:")
print(serialize_bitarray(clks_a[0]))

original:
bitarray('1111111100101100001100011011110111100111001111111000111110010100011101111111111110111000110111111110111101011111111001011111011110111011101111001101011101100111101110001101101101010011001100110011010111110011010100101010111011111100101000111111101101111011100011100111110011110110110011110001010101101011011111111011011111110101100110010101111101111111101110001111110111111101010111100101110111100110111110100100110001100010110110111101101111011010111111110011110100101010111111110111011111100110111011111100001011111100011110000101010111111011101111011110110110001000100111111111111011101111101100111110111111011011001111100011111110111110100101101001000100011110101001000010101001110110111111111001111111111111010101011001110110101010110101100110110111000111111110111111000010111111000111110011111000100101111111111011111001111100011001101000110010111110111010001111111101110100101110001111001011111011111111011010110011011011001011010101011111111011011111110101111001101111010101111111011101111010001101110011101110111101')
serialized:
/ywxvec/j5R3/7jf71/l97u812e421MzNfNSrvyj+3uOfPbPFWt/t/WZX3+4/f1eXeb6TGLb29r/PSr/d+bvwvx4Vfu97Yif/u+z79s+P76WkR6kKnb/n/9VnarWbcf78L8fPiX/vnxmjL7o/3S48vv9rNstV/t/Xm9X93o3O70=

Hash data set B

Now we hash the second dataset using the same keys and same schema.

b_csv = io.StringIO()
dfB.to_csv(b_csv)
b_csv.seek(0)
clks_b = clkhash.clk.generate_clk_from_csv(b_csv, secret, schema)

generating CLKs: 100%|██████████| 5.00k/5.00k [00:01<00:00, 2.58kclk/s, mean=687, std=30.4]

len(clks_b)

5000

Find matches between the two sets of CLKs

We have generated two sets of CLKs which represent entity information in a privacy-preserving way. The more similar two CLKs are, the more likely it is that they represent the same entity.

For this task we will use anonlink, a Python (and optimised C++) implementation of anonymous linkage using CLKs.

Using anonlink we find the candidate pairs - which is all possible pairs above the given threshold. Then we solve for the most likely mapping.

import anonlink

def mapping_from_clks(clks_a, clks_b, threshold):
    results_candidate_pairs = anonlink.candidate_generation.find_candidate_pairs(
            [clks_a, clks_b],
            anonlink.similarities.dice_coefficient,
            threshold
    )
    solution = anonlink.solving.greedy_solve(results_candidate_pairs)
    print('Found {} matches'.format(len(solution)))
    # each entry in `solution` looks like this: '((0, 4039), (1, 2689))'.
    # The format is ((dataset_id, row_id), (dataset_id, row_id))
    # As we only have two parties in this example, we can remove the dataset_ids.
    # Also, turning the solution into a set will make it easier to assess the
    # quality of the matching.
    return set((a, b) for ((_, a), (_, b)) in solution)

found_matches = mapping_from_clks(clks_a, clks_b, 0.9)

Found 4049 matches

Evaluate matching quality

Let’s investigate some of those matches and the overall matching quality

Fortunately, the febrl4 datasets contain record ids which tell us the correct linkages. Using this information we are able to create a set of the true matches.

# rec_id in dfA has the form 'rec-1070-org'. We only want the number. Additionally, as we are
# interested in the position of the records, we create a new index which contains the row numbers.
dfA_ = dfA.rename(lambda x: x[4:-4], axis='index').reset_index()
dfB_ = dfB.rename(lambda x: x[4:-6], axis='index').reset_index()
# now we can merge dfA_ and dfB_ on the record_id.
a = pd.DataFrame({'ida': dfA_.index, 'rec_id': dfA_['rec_id']})
b = pd.DataFrame({'idb': dfB_.index, 'rec_id': dfB_['rec_id']})
dfj = a.merge(b, on='rec_id', how='inner').drop(columns=['rec_id'])
# and build a set of the corresponding row numbers.
true_matches = set((row[0], row[1]) for row in dfj.itertuples(index=False))

def describe_matching_quality(found_matches, show_examples=False):
    if show_examples:
        print('idx_a, idx_b,     rec_id_a,       rec_id_b')
        print('---------------------------------------------')
        for a_i, b_i in itertools.islice(found_matches, 10):
            print('{:4d}, {:5d}, {:>11}, {:>14}'.format(a_i+1, b_i+1, a.iloc[a_i]['rec_id'], b.iloc[b_i]['rec_id']))
        print('---------------------------------------------')

    tp = len(found_matches & true_matches)
    fp = len(found_matches - true_matches)
    fn = len(true_matches - found_matches)

    precision = tp / (tp + fp)
    recall = tp / (tp + fn)

    print('Precision: {:.3f}, Recall: {:.3f}'.format(precision, recall))

describe_matching_quality(found_matches, show_examples=True)

idx_a, idx_b,     rec_id_a,       rec_id_b
---------------------------------------------
3170,   259,        3730,           3730
1685,  3323,        2888,           2888
 733,  2003,        4239,           4239
4550,  3627,        4216,           4216
1875,  2991,        4391,           4391
3928,  2377,        3493,           3493
4928,  4656,         276,            276
 334,   945,        4848,           4848
2288,  4331,        3491,           3491
4088,  2454,        1850,           1850
---------------------------------------------
Precision: 1.000, Recall: 0.810

Precision tells us about how many of the found matches are actual matches. The score of 1.0 means that we did perfectly in this respect, however, recall, the measure of how many of the actual matches were correctly identified, is quite low with only 81%.

Let’s go back to the mapping calculation (mapping_from_clks) an reduce the value for threshold to 0.8.

found_matches = mapping_from_clks(clks_a, clks_b, 0.8)
describe_matching_quality(found_matches)

Found 4962 matches
Precision: 1.000, Recall: 0.992

Great, for this threshold value we get a precision of 100% and a recall of 99.2%.

The explanation is that when the information about an entity differs slightly in the two datasets (e.g. spelling errors, abbrevations, missing values, …) then the corresponding CLKs will differ in some number of bits as well. It is important to choose an appropriate threshold for the amount of perturbations present in the data (a threshold of 0.72 and below generates an almost perfect mapping with little mistakes).

This concludes the tutorial. Feel free to go back to the CLK generation and experiment on how different setting will affect the matching quality.