CompFE

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 GenerateSubsets.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] [number of subsets to test] [start_index] which takes as input the selected subsets from GenerateSubsets.py. See above for modifications. Note this can take a very long time even parallelized which is why we offer a starting index.
• 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] [number_templates_to_average]. See above for modifications.