Computational Fuzzy Extractors
Copyright 2024 Sohaib Ahmad, Luke Demarest, Benjamin Fuller, and Sailesh Simhadri
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Files:
- bh_iris.ipynb Trains feature extractor
- PythonImpl/FuzzyExtractor.py legacy implementation of Canetti et al. fuzzy extractor sample-then-lock from https://eprint.iacr.org/2014/243
- PythonImpl/RPISetDifference.py implementation of set difference construction from https://eprint.iacr.org/2016/1100
- Statistics folder is used to understand quality of zeta sampling algorithm.
- subsets contains selected subsets for mid and high security parameters.
GenerateSubsets.py
is a file that generates selected subsets and can be used as follows:python3 GenerateSubsetsNewFE.py [subset size] ['simple' or 'complex'] [alpha] [number of subsets] [output file name]
and will be looking for a feature vector folder (which should be provided by the user) with each class having a folder and each vector having a file with a distinct filename that begins with the class name and a confidence file where each index has a line documenting the agreement with inter and intra class comparisons. Optionally, you may manually exclude indices from the complex confidence selection by providing indices in the bad_list variable. (Modification toread_complex_conf
will be necessary to match your file structure and folder structure.)EntropyTest.py
is the test that calculates the entropy of a sample of your subsets and should be used as follows"python3 EntropyTest.py [subset size or entropy threshold] [number of subsets] [subsets filename]
which takes as input the selected subsets fromGenerateSubsets.py
. See above for modifications.TARTest.py
is the test that calculates the TAR for your subsets and should be used as follows:python3 TARTest.py [subset size] [number of subsets] [subsets filename] [subsets to test] [starting index]
. See above for modifications. Note this can take a very long time even parallelized which is why we offer a starting index.