CompFE

Computational Fuzzy Extractors

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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 to read_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 from GenerateSubsets.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.

benjaminfuller/CompFE

CompFE