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        "author": "",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: POSIX",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Security :: Cryptography"
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        "description": "\n.. image:: https://travis-ci.org/data61/anonlink.svg?branch=master\n    :target: https://travis-ci.org/data61/anonlink\n\n\n.. image:: https://codecov.io/gh/data61/anonlink/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/data61/anonlink\n\n\n.. image:: https://requires.io/github/data61/anonlink/requirements.svg?branch=master\n    :target: https://requires.io/github/data61/anonlink/requirements/?branch=master\n\nA Python (and optimised C++) implementation of **anonymous linkage** using\n*cryptographic linkage keys* as described by Rainer Schnell, Tobias\nBachteler, and J\u00f6rg Reiher in `A Novel Error-Tolerant Anonymous Linking\nCode <http://grlc.german-microsimulation.de/wp-content/uploads/2017/05/downloadwp-grlc-2011-02.pdf>`__.\n\n``anonlink`` computes similarity scores, and/or best guess matches between sets\nof *cryptographic linkage keys* (hashed entity records).\n\nUse `clkhash <https://github.com/data61/clkhash>`__ to create cryptographic linkage keys\nfrom personally identifiable data.\n\nInstallation\n============\n\nInstall directly from PyPi::\n\n    pip install anonlink\n\nOr to install from source::\n\n    pip install -r requirements.txt\n    pip install -e .\n\nAlternative - Manually compile the C++ library\n----------------------------------------------\n\nFor mac with:\n\n::\n\n    g++ -std=c++11 -mssse3 -mpopcnt -O2 -Wall -pedantic -Wextra -dynamiclib -fpic -o _entitymatcher.dll dice_one_against_many.cpp\n\nFor linux with:\n\n::\n\n    g++ -std=c++11 -mssse3 -mpopcnt -O2 -Wall -pedantic -Wextra -shared -fpic -o _entitymatcher.so dice_one_against_many.cpp\n\nBenchmark\n---------\n\nYou can run the benchmark with:\n\n::\n\n    $ python -m anonlink.benchmark\n    Anonlink benchmark -- see README for explanation\n    ------------------------------------------------\n    100000 x 1024 bit popcounts\n    Implementation              | Time (ms) | Bandwidth (MiB/s) | Throughput (1e6 popc/s)\n    Python (bitarray.count()):  |    17.78  |      686.54       |    5.62\n    Native code (no copy):      |     1.00  |    12243.76       |  100.30\n    Native code (w/ copy):      |   344.17  |       35.47       |    0.29 (99.7% copying)\n\n    Threshold: 0.5\n    Size 1 | Size 2 | Comparisons      | Total Time (s)          | Throughput\n           |        |        (match %) | (comparisons / matching)|  (1e6 cmp/s)\n    -------+--------+------------------+-------------------------+-------------\n      1000 |   1000 |    1e6  (50.20%) |  0.249  (88.6% / 11.4%) |     4.525\n      2000 |   2000 |    4e6  (50.51%) |  1.069  (88.5% / 11.5%) |     4.227\n      3000 |   3000 |    9e6  (50.51%) |  2.412  (85.3% / 14.7%) |     4.375\n      4000 |   4000 |   16e6  (50.56%) |  4.316  (83.6% / 16.4%) |     4.434\n\n    Threshold: 0.7\n    Size 1 | Size 2 | Comparisons      | Total Time (s)          | Throughput\n           |        |        (match %) | (comparisons / matching)|  (1e6 cmp/s)\n    -------+--------+------------------+-------------------------+-------------\n      1000 |   1000 |    1e6  ( 0.01%) |  0.017  (99.8% /  0.2%) |    59.605\n      2000 |   2000 |    4e6  ( 0.01%) |  0.056  (99.8% /  0.2%) |    71.484\n      3000 |   3000 |    9e6  ( 0.01%) |  0.118  (99.9% /  0.1%) |    76.500\n      4000 |   4000 |   16e6  ( 0.01%) |  0.202  (99.9% /  0.1%) |    79.256\n      5000 |   5000 |   25e6  ( 0.01%) |  0.309  (99.9% /  0.1%) |    81.093\n      6000 |   6000 |   36e6  ( 0.01%) |  0.435  (99.9% /  0.1%) |    82.841\n      7000 |   7000 |   49e6  ( 0.01%) |  0.590  (99.9% /  0.1%) |    83.164\n      8000 |   8000 |   64e6  ( 0.01%) |  0.757  (99.9% /  0.1%) |    84.619\n      9000 |   9000 |   81e6  ( 0.01%) |  0.962  (99.8% /  0.2%) |    84.358\n     10000 |  10000 |  100e6  ( 0.01%) |  1.166  (99.8% /  0.2%) |    85.895\n     20000 |  20000 |  400e6  ( 0.01%) |  4.586  (99.9% /  0.1%) |    87.334\n\nThe tables are interpreted as follows. The first section compares the\nbandwidth doing popcounts through (i) the Python bitarray library and\n(ii) a native code implementation in assembler.  The latter\nimplementation is measured in two ways: the first measures just the\ntime taken to compute the popcounts, while the second includes the\ntime taken to copy the data out of the running Python instance as well\nas copying the result back into Python. The \"% copying\" measure is the\nproportion of time spent doing this copying.\n\nThe second section includes two tables that measure the throughput of\nthe Dice coefficient comparison function. The two tables correspond to\ntwo different choices of \"matching threshold\", 0.5 and 0.7, which were\nchosen to characterise two different performance scenarios. Since the\ndata used for comparisons is randomly generated, the first threshold\nvalue will cause about 50% of the candidates to \"match\", while the\nsecond threshold value will cause <0.01% of the candidates to match\n(these values are reported in the \"match %\" column).  In both cases,\nall matches above the threshold are returned and passed to the\nsolver. In the first case, the large number of matches means that much\nof the time is spent keeping the candidates in order so that the top\n`k` matches can be returned. In the latter case, the tiny number of\ncandidate matches means that the throughput is determined primarily by\nthe comparison code itself.\n\nFinally, the Total Time column includes indications as to the\nproportion of time spent calculating the (sparse) similarity matrix\n`comparisons` and the proportion of time spent `matching` in the\ngreedy solver. This latter is determined by the size of the similarity\nmatrix, which will be approximately `#comparisons * match% / 100`.\n\nTests\n=====\n\nRun unit tests with `pytest`:\n\n::\n\n    $ pytest\n    ====================================== test session starts ======================================\n    platform linux -- Python 3.6.4, pytest-3.2.5, py-1.4.34, pluggy-0.4.0\n    rootdir: /home/hlaw/src/n1-anonlink, inifile:\n    collected 71 items\n\n    tests/test_benchmark.py ...\n    tests/test_bloommatcher.py ..............\n    tests/test_e2e.py .............ss....\n    tests/test_matcher.py ..x.....x......x....x..\n    tests/test_similarity.py .........\n    tests/test_util.py ...\n\n    ======================== 65 passed, 2 skipped, 4 xfailed in 4.01 seconds ========================\n\nTo enable slightly larger tests add the following environment variables:\n\n-  INCLUDE_10K\n-  INCLUDE_100K\n\nLimitations\n-----------\n\n-  The linkage process has order n^2 time complexity - although algorithms exist to\n   significantly speed this up. Several possible speedups are described\n   in `Privacy Preserving Record Linkage with PPJoin <http://dbs.uni-leipzig.de/file/P4Join-BTW2015.pdf>`__.\n\n\nDiscussion\n----------\n\nIf you run into bugs, you can file them in our `issue tracker <https://github.com/data61/anonlink/issues>`__\non GitHub.\n\nThere is also an `anonlink mailing list <https://groups.google.com/forum/#!forum/anonlink>`__\nfor development discussion and release announcements.\n\nWherever we interact, we strive to follow the `Python Community Code of Conduct <https://www.python.org/psf/codeofconduct/>`__.\n\n\nLicense\n-------\n\nCopyright 2017 CSIRO (Data61)\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n\n",
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        "license": "Apache",
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