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+"""
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+ Health Information Privacy Lab
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+ Brad. Malin, Weiyi Xia, Steve L. Nyemba
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+
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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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+
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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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+
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+ This framework integrates pandas (for now) as an extension and can be used in two modes :
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+ Experimental mode
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+ Here the assumption is that we are not sure of the attributes to be disclosed, the framework will explore a variety of combinations and associate risk measures every random combinations
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+
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+ Evaluation mode
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+ The evaluation mode assumes the set of attributes given are known and thus will evaluate risk for a subset of attributes.
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+
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+ features :
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+ - determine viable fields (quantifiable in terms of uniqueness). This is a way to identify fields that can act as identifiers.
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+ - explore and evaluate risk of a sample dataset against a known population dataset
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+ - explore and evaluate risk on a sample dataset
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+ Usage:
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+ from pandas_risk import *
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+
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+ mydataframe = pd.DataFrame('/myfile.csv')
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+ resp = mydataframe.risk.evaluate(id=<name of patient field>,num_runs=<number of runs>,cols=[])
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+ resp = mydataframe.risk.explore(id=<name of patient field>,num_runs=<number of runs>,cols=[])
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+
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+
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+ @TODO:
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+ - Provide a selected number of fields and risk will be computed for those fields.
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+ - include journalist risk
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+
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+"""
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+import pandas as pd
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+import numpy as np
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+import logging
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+import json
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+from datetime import datetime
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+import sys
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+
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+from itertools import combinations
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+
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+@pd.api.extensions.register_dataframe_accessor("risk")
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+class deid :
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+
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+ """
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+ This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe
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+ """
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+ def __init__(self,df):
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+ self._df = df.fillna(' ')
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+ #
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+ # Let's get the distribution of the values so we know what how unique the fields are
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+ #
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+ values = df.apply(lambda col: col.unique().size / df.shape[0])
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+ self._dinfo = dict(zip(df.columns.tolist(),values))
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+
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+ def explore(self,**args):
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+ """
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+ This function will perform experimentation by performing a random policies (combinations of attributes)
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+ This function is intended to explore a variety of policies and evaluate their associated risk.
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+
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+ :pop|sample data-frame with population or sample reference
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+ :field_count number of fields to randomly select
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+ :strict if set the field_count is exact otherwise field_count is range from 2-field_count
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+ :num_runs number of runs (by default 5)
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+ """
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+
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+ pop= args['pop'] if 'pop' in args else None
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+
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+ if 'pop_size' in args :
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+ pop_size = np.float64(args['pop_size'])
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+ else:
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+ pop_size = -1
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+
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+
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+ #
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+ # Policies will be generated with a number of runs
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+ #
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+ RUNS = args['num_runs'] if 'num_runs' in args else 5
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+
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+ sample = args['sample'] if 'sample' in args else pd.DataFrame(self._df)
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+
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+ k = sample.columns.size if 'field_count' not in args else int(args['field_count']) +1
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+ #
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+ # remove fields that are unique, they function as identifiers
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+ #
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+ if 'id' in args :
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+ id = args['id']
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+ columns = list(set(sample.columns.tolist()) - set([id]))
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+ else:
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+ columns = sample.columns.tolist()
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+
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+ # If columns are not specified we can derive them from self._dinfo
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+ # given the distribution all fields that are < 1 will be accounted for
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+ # columns = args['cols'] if 'cols' in args else [key for key in self._dinfo if self._dinfo[key] < 1]
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+
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+ o = pd.DataFrame()
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+ columns = [key for key in self._dinfo if self._dinfo[key] < 1]
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+ _policy_count = 2 if 'policy_count' not in args else int(args['policy_count'])
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+
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+ _policies = []
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+ _index = 0
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+ for size in np.arange(2,len(columns)) :
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+ p = list(combinations(columns,size))
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+ p = (np.array(p)[ np.random.choice( len(p), _policy_count)].tolist())
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+ flag = 'Policy_'+str(_index)
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+ _index += 1
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+ for cols in p :
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+ r = self.evaluate(sample=sample,cols=cols,flag = flag)
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+ p = pd.DataFrame(1*sample.columns.isin(cols)).T
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+ p.columns = sample.columns
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+ o = pd.concat([o,r.join(p)])
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+
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+
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+ # for i in np.arange(RUNS):
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+ # if 'strict' not in args or ('strict' in args and args['strict'] is False):
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+ # n = np.random.randint(2,k)
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+ # else:
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+ # n = args['field_count']
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+ # cols = np.random.choice(columns,n,replace=False).tolist()
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+ # params = {'sample':sample,'cols':cols}
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+ # if pop is not None :
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+ # params['pop'] = pop
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+ # if pop_size > 0 :
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+ # params['pop_size'] = pop_size
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+
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+ # r = self.evaluate(**params)
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+ # #
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+ # # let's put the policy in place
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+ # p = pd.DataFrame(1*sample.columns.isin(cols)).T
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+ # p.columns = sample.columns
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+ # # o = o.append(r.join(p))
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+ # o = pd.concat([o,r.join(p)])
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+
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+
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+ o.index = np.arange(o.shape[0]).astype(np.int64)
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+
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+ return o
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+ def evaluate(self, **args):
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+ """
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+ This function has the ability to evaluate risk associated with either a population or a sample dataset
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+ :sample sample dataset
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+ :pop population dataset
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+ :cols list of columns of interest or policies
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+ :flag user provided flag for the context of the evaluation
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+ """
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+ if 'sample' in args :
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+ sample = pd.DataFrame(args['sample'])
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+ else:
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+ sample = pd.DataFrame(self._df)
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+
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+ if not args or 'cols' not in args:
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+ # cols = sample.columns.tolist()
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+ cols = [key for key in self._dinfo if self._dinfo[key] < 1]
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+ elif args and 'cols' in args:
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+ cols = args['cols']
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+ #
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+ #
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+
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+ flag = 'UNFLAGGED' if 'flag' not in args else args['flag']
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+ #
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+ # @TODO: auto select the columns i.e removing the columns that will have the effect of an identifier
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+ #
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+ # if 'population' in args :
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+ # pop = pd.DataFrame(args['population'])
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+ r = {"flag":flag}
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+ # if sample :
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+
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+ handle_sample = Sample()
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+ xi = sample.groupby(cols,as_index=False).count().values
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+
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+ handle_sample.set('groups',xi)
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+ if 'pop_size' in args :
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+ pop_size = np.float64(args['pop_size'])
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+ else:
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+ pop_size = -1
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+ #
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+ #-- The following conditional line is to address the labels that will be returned
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+ # @TODO: Find a more elegant way of doing this.
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+ #
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+
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+ if 'pop' in args :
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+ label_market = 'sample marketer'
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+ label_prosec = 'sample prosecutor'
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+ label_groupN = 'sample group count'
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+ label_unique = 'sample journalist' #'sample unique ratio'
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+ # r['sample marketer'] = handle_sample.marketer()
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+ # r['sample prosecutor'] = handle_sample.prosecutor()
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+ # r['sample unique ratio'] = handle_sample.unique_ratio()
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+ # r['sample group count'] = xi.size
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+ # r['sample group count'] = len(xi)
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+ else:
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+ label_market = 'marketer'
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+ label_prosec = 'prosecutor'
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+ label_groupN = 'group count'
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+ label_unique = 'journalist' #'unique ratio'
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+ # r['marketer'] = handle_sample.marketer()
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+ # r['prosecutor'] = handle_sample.prosecutor()
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+ # r['unique ratio'] = handle_sample.unique_ratio()
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+ # r['group count'] = xi.size
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+ # r['group count'] = len(xi)
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+ if pop_size > 0 :
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+ handle_sample.set('pop_size',pop_size)
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+ r['pitman risk'] = handle_sample.pitman()
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+ r[label_market] = handle_sample.marketer()
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+ r[label_unique] = handle_sample.unique_ratio()
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+ r[label_prosec] = handle_sample.prosecutor()
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+ r[label_groupN] = len(xi)
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+
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+ if 'pop' in args :
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+ xi = pd.DataFrame({"sample_group_size":sample.groupby(cols,as_index=False).count()}).reset_index()
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+ yi = pd.DataFrame({"population_group_size":args['pop'].groupby(cols,as_index=False).size()}).reset_index()
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+ merged_groups = pd.merge(xi,yi,on=cols,how='inner')
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+ handle_population= Population()
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+ handle_population.set('merged_groups',merged_groups)
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+
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+ r['pop. marketer'] = handle_population.marketer()
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+ r['pitman risk'] = handle_population.pitman()
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+ r['pop. group size'] = np.unique(yi.population_group_size).size
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+ #
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+ # At this point we have both columns for either sample,population or both
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+ #
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+ r['field count'] = len(cols)
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+ return pd.DataFrame([r])
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+
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+class Risk :
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+ """
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+ This class is an abstraction of how we chose to structure risk computation i.e in 2 sub classes:
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+ - Sample computes risk associated with a sample dataset only
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+ - Population computes risk associated with a population
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+ """
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+ def __init__(self):
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+ self.cache = {}
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+ def set(self,key,value):
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+ if id not in self.cache :
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+ self.cache[id] = {}
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+ self.cache[key] = value
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+
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+class Sample(Risk):
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+ """
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+ This class will compute risk for the sample dataset: the marketer and prosecutor risk are computed by default.
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+ This class can optionally add pitman risk if the population size is known.
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+ """
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+ def __init__(self):
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+ Risk.__init__(self)
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+ def marketer(self):
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+ """
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+ computing marketer risk for sample dataset
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+ """
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+
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+
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+ groups = self.cache['groups']
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+ # group_count = groups.size
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+ # row_count = groups.sum()
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+ group_count = len(groups)
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+ row_count = np.sum([_g[-1] for _g in groups])
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+ return group_count / np.float64(row_count)
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+
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+ def prosecutor(self):
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+ """
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+ The prosecutor risk consists in determining 1 over the smallest group size
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+ It identifies if there is at least one record that is unique
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+ """
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+ groups = self.cache['groups']
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+ _min = np.min([_g[-1] for _g in groups])
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+ # return 1 / np.float64(groups.min())
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+ return 1/ np.float64(_min)
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+ def unique_ratio(self):
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+ groups = self.cache['groups']
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+ # row_count = groups.sum()
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+ row_count = np.sum([_g[-1] for _g in groups])
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+ # return groups[groups == 1].sum() / np.float64(row_count)
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+ values = [_g[-1] for _g in groups if _g[-1] == 1]
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+
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+ return np.sum(values) / np.float64(row_count)
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+
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+ def pitman(self):
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+ """
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+ This function will approximate pitman de-identification risk based on pitman sampling
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+ """
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+ groups = self.cache['groups']
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+ si = groups[groups == 1].size
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+ # u = groups.size
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+ u = len(groups)
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+ alpha = np.divide(si , np.float64(u) )
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+ row_count = np.sum([_g[-1] for _g in groups])
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+ # f = np.divide(groups.sum(), np.float64(self.cache['pop_size']))
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+ f = np.divide(row_count, np.float64(self.cache['pop_size']))
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+ return np.power(f,1-alpha)
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+
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+class Population(Sample):
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+ """
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+ This class will compute risk for datasets that have population information or datasets associated with them.
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+ This computation includes pitman risk (it requires minimal information about population)
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+ """
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+ def __init__(self,**args):
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+ Sample.__init__(self)
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+
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+ def set(self,key,value):
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+ Sample.set(self,key,value)
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+ if key == 'merged_groups' :
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+
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+ Sample.set(self,'pop_size',np.float64(value.population_group_size.sum()) )
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+ Sample.set(self,'groups',value.sample_group_size)
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+ """
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+ This class will measure risk and account for the existance of a population
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+ :merged_groups {sample_group_size, population_group_size} is a merged dataset with group sizes of both population and sample
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+ """
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+ def marketer(self):
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+ """
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+ This function requires
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+ """
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+ r = self.cache['merged_groups']
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+ sample_row_count = r.sample_group_size.sum()
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+ #
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+ # @TODO : make sure the above line is size (not sum)
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+ # sample_row_count = r.sample_group_size.size
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+ return r.apply(lambda row: (row.sample_group_size / np.float64(row.population_group_size)) /np.float64(sample_row_count) ,axis=1).sum()
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+
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+
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Steve Nyemba