anonlink 0.15.3

Description:

anonlink 0.15.3

A Python (and optimised C++) implementation of anonymous linkage using
cryptographic linkage keys as described by Rainer Schnell, Tobias
Bachteler, and Jörg Reiher in A Novel Error-Tolerant Anonymous Linking
Code.
anonlink computes similarity scores, and/or best guess matches between sets
of cryptographic linkage keys (hashed entity records).
Use clkhash to create cryptographic linkage keys
from personally identifiable data.

Installation
Install a precompiled wheel from PyPi:
pip install anonlink
Or (if your system has a C++ compiler) you can locally install from source:
pip install -r requirements.txt
pip install -e .

Benchmark
You can run the benchmark with:
$ python -m anonlink.benchmark
Anonlink benchmark -- see README for explanation
------------------------------------------------

Threshold: 0.5, All results returned
Size 1 | Size 2 | Comparisons | Total Time (s) | Throughput
| | (match %) | (comparisons / matching)| (1e6 cmp/s)
-------+--------+------------------+-------------------------+-------------
1000 | 1000 | 1e6 (50.73%) | 0.762 (49.2% / 50.8%) | 2.669
2000 | 2000 | 4e6 (51.04%) | 3.696 (42.6% / 57.4%) | 2.540
3000 | 3000 | 9e6 (50.25%) | 8.121 (43.5% / 56.5%) | 2.548
4000 | 4000 | 16e6 (50.71%) | 15.560 (41.1% / 58.9%) | 2.504

Threshold: 0.5, Top 100 matches per record returned
Size 1 | Size 2 | Comparisons | Total Time (s) | Throughput
| | (match %) | (comparisons / matching)| (1e6 cmp/s)
-------+--------+------------------+-------------------------+-------------
1000 | 1000 | 1e6 ( 6.86%) | 0.170 (85.9% / 14.1%) | 6.846
2000 | 2000 | 4e6 ( 3.22%) | 0.384 (82.9% / 17.1%) | 12.561
3000 | 3000 | 9e6 ( 2.09%) | 0.612 (81.6% / 18.4%) | 18.016
4000 | 4000 | 16e6 ( 1.52%) | 0.919 (78.7% / 21.3%) | 22.135
5000 | 5000 | 25e6 ( 1.18%) | 1.163 (80.8% / 19.2%) | 26.592
6000 | 6000 | 36e6 ( 0.97%) | 1.535 (75.4% / 24.6%) | 31.113
7000 | 7000 | 49e6 ( 0.82%) | 1.791 (80.6% / 19.4%) | 33.951
8000 | 8000 | 64e6 ( 0.71%) | 2.095 (81.5% / 18.5%) | 37.466
9000 | 9000 | 81e6 ( 0.63%) | 2.766 (72.5% / 27.5%) | 40.389
10000 | 10000 | 100e6 ( 0.56%) | 2.765 (81.7% / 18.3%) | 44.277
20000 | 20000 | 400e6 ( 0.27%) | 7.062 (86.2% / 13.8%) | 65.711

Threshold: 0.7, All results returned
Size 1 | Size 2 | Comparisons | Total Time (s) | Throughput
| | (match %) | (comparisons / matching)| (1e6 cmp/s)
-------+--------+------------------+-------------------------+-------------
1000 | 1000 | 1e6 ( 0.01%) | 0.009 (99.0% / 1.0%) | 113.109
2000 | 2000 | 4e6 ( 0.01%) | 0.033 (98.7% / 1.3%) | 124.076
3000 | 3000 | 9e6 ( 0.01%) | 0.071 (99.1% / 0.9%) | 128.515
4000 | 4000 | 16e6 ( 0.01%) | 0.123 (99.0% / 1.0%) | 131.654
5000 | 5000 | 25e6 ( 0.01%) | 0.202 (99.1% / 0.9%) | 124.999
6000 | 6000 | 36e6 ( 0.01%) | 0.277 (99.0% / 1.0%) | 131.403
7000 | 7000 | 49e6 ( 0.01%) | 0.368 (98.9% / 1.1%) | 134.428
8000 | 8000 | 64e6 ( 0.01%) | 0.490 (99.0% / 1.0%) | 131.891
9000 | 9000 | 81e6 ( 0.01%) | 0.608 (99.0% / 1.0%) | 134.564
10000 | 10000 | 100e6 ( 0.01%) | 0.753 (99.0% / 1.0%) | 134.105
20000 | 20000 | 400e6 ( 0.01%) | 2.905 (98.8% / 1.2%) | 139.294

Threshold: 0.7, Top 100 matches per record returned
Size 1 | Size 2 | Comparisons | Total Time (s) | Throughput
| | (match %) | (comparisons / matching)| (1e6 cmp/s)
-------+--------+------------------+-------------------------+-------------
1000 | 1000 | 1e6 ( 0.01%) | 0.009 (99.0% / 1.0%) | 111.640
2000 | 2000 | 4e6 ( 0.01%) | 0.033 (98.6% / 1.4%) | 122.060
3000 | 3000 | 9e6 ( 0.01%) | 0.074 (99.1% / 0.9%) | 123.237
4000 | 4000 | 16e6 ( 0.01%) | 0.124 (99.0% / 1.0%) | 130.204
5000 | 5000 | 25e6 ( 0.01%) | 0.208 (99.1% / 0.9%) | 121.351
6000 | 6000 | 36e6 ( 0.01%) | 0.275 (99.0% / 1.0%) | 132.186
7000 | 7000 | 49e6 ( 0.01%) | 0.373 (99.0% / 1.0%) | 132.650
8000 | 8000 | 64e6 ( 0.01%) | 0.496 (99.1% / 0.9%) | 130.125
9000 | 9000 | 81e6 ( 0.01%) | 0.614 (99.0% / 1.0%) | 133.216
10000 | 10000 | 100e6 ( 0.01%) | 0.775 (99.1% / 0.9%) | 130.230
20000 | 20000 | 400e6 ( 0.01%) | 2.939 (98.9% / 1.1%) | 137.574
The tables are interpreted as follows. Each table measures the throughput
of the Dice coefficient comparison function. The four tables correspond to
two different choices of “matching threshold” and “result limiting”.
These parameters have been chosen to characterise two different performance
scenarios. Since the data used for comparisons is randomly generated, the
first threshold value (0.5) will cause about 50% of the candidates to
“match”, while the second threshold value (0.7) will cause ~0.01% of the
candidates to match (these values are reported in the “match %” column).
Where the table heading includes “All results returned”, all matches above
the threshold are returned and passed to the solver.
With the threshold of 0.5, the large number of matches means that much
of the time is spent keeping the candidates in order. Next we limit the
number of matches per record to the top 100 - which also must be above the
threshold.
In the final two tables we use the threshold value of 0.7, this very
effectively filters the number of candidate matches down. Here the throughput
is determined primarily by the comparison code itself, adding the top 100
filter has no major impact.
Finally, the Total Time column includes indications as to the
proportion of time spent calculating the (sparse) similarity matrix
comparisons and the proportion of time spent matching in the
greedy solver. This latter is determined by the size of the similarity
matrix, which will be approximately #comparisons * match% / 100.



Tests
Run unit tests with pytest:
$ pytest
====================================== test session starts ======================================
platform linux -- Python 3.6.4, pytest-3.2.5, py-1.4.34, pluggy-0.4.0
rootdir: /home/hlaw/src/n1-anonlink, inifile:
collected 71 items

tests/test_benchmark.py ...
tests/test_bloommatcher.py ..............
tests/test_e2e.py .............ss....
tests/test_matcher.py ..x.....x......x....x..
tests/test_similarity.py .........
tests/test_util.py ...

======================== 65 passed, 2 skipped, 4 xfailed in 4.01 seconds ========================
To enable slightly larger tests add the following environment variables:

INCLUDE_10K
INCLUDE_100K


Limitations

The linkage process has order n^2 time complexity - although algorithms exist to
significantly speed this up. Several possible speedups are described
in Privacy Preserving Record Linkage with PPJoin.



Discussion
If you run into bugs, you can file them in our issue tracker
on GitHub.
There is also an anonlink mailing list
for development discussion and release announcements.
Wherever we interact, we strive to follow the Python Community Code of Conduct.



Citing
Anonlink is designed, developed and supported by CSIRO’s Data61. If you use any part
of this library in your research, please cite it using the following BibTex entry:
@misc{Anonlink,
author = {CSIRO's Data61},
title = {Anonlink Private Record Linkage System},
year = {2017},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/data61/anonlink}},
}

License
Copyright 2017 CSIRO (Data61)
Licensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.