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52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
"""
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(c) 2019, Health Information Privacy Lab
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Brad. Malin, Weiyi Xia, Steve L. Nyemba
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This framework computes re-identification risk of a dataset assuming the data being shared can be loaded into a dataframe (pandas)
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The framework will compute the following risk measures:
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- marketer
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- prosecutor
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- pitman
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References :
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https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf
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This framework integrates pandas (for now) as an extension and can be used in two modes :
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1. explore:
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Here the assumption is that we are not sure of the attributes to be disclosed,
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The framework will explore a variety of combinations and associate risk measures every random combinations it can come up with
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2. evaluation
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Here the assumption is that we are clear on the sets of attributes to be used and we are interested in computing the associated risk.
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Four risk measures are computed :
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- Marketer risk
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- Prosecutor risk
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- Journalist risk
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- Pitman Risk
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Usage:
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import numpy as np
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import pandas as pd
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from pandas_risk import *
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mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50) })
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print mydf.risk.evaluate()
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#
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# computing journalist and pitman
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# - Insure the population size is much greater than the sample size
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# - Insure the fields are identical in both sample and population
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#
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pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"q":np.random.choice( np.random.randint(1,10),150)})
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mydf.risk.evaluate(pop=pop)
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@TODO:
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- Evaluation of how sparse attributes are (the ratio of non-null over rows)
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- Have a smart way to drop attributes (based on the above in random policy search)
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"""
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from risk import risk
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