Package: anonlink
A Python (and optimised C++) implementation ofanonymous linkageusingcryptographic linkage keysas described by Rainer Schnell, Tobias
Bachteler, and Jörg Reiher inA Novel Error-Tolerant Anonymous Linking
Code.anonlinkcomputes similarity scores, and/or best guess matches between sets
ofcryptographic linkage keys(hashed entity records).Useclkhashto create cryptographic linkage keys
from personally identifiable data.InstallationInstall a precompiled wheel from PyPi:pip install anonlinkOr (if your system has a C++ compiler) you can locally install from source:pip install -r requirements.txt
pip install -e .BenchmarkYou 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%) |   137.574The 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 of0.5, 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 of0.7, 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 matrixcomparisonsand the proportion of time spentmatchingin the
greedy solver. This latter is determined by the size of the similarity
matrix, which will be approximately#comparisons * match% / 100.TestsRun unit tests withpytest:$ 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_10KINCLUDE_100KLimitationsThe linkage process has order n^2 time complexity - although algorithms exist to
significantly speed this up. Several possible speedups are described
inPrivacy Preserving Record Linkage with PPJoin.DiscussionIf you run into bugs, you can file them in ourissue trackeron GitHub.There is also ananonlink mailing listfor development discussion and release announcements.Wherever we interact, we strive to follow thePython Community Code of Conduct.CitingAnonlink is designed, developed and supported byCSIRO’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}},
}LicenseCopyright 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 athttp://www.apache.org/licenses/LICENSE-2.0Unless 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.