lnyemba / privacykit

The code extends pandas to compute re-identification risk given industry standard risk measures

Re-Identification Risk

This framework computes re-identification risk of a dataset by extending pandas. It works like a pandas add-on The framework will compute the following risk measures: marketer, prosecutor, journalist and pitman risk. References for the risk measures can be found on [http://ehelthinformation.ca] (http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf) and https://www.scb.se/contentassets

There are two modes available :

explore:

Here the assumption is that we are not sure of the attributes to be disclosed, the framework will randomly generate random combinations of attributes and evaluate them accordingly as it provides all the measures of risk.

evaluation

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.

Four risk measures are computed :

• Marketer risk
• Prosecutor risk
• Journalist risk
• Pitman Risk Video tutorial,by Dr. Weiyi Xia and Publication by Dr. Nobuaki Hoshino

Usage:

Install this package using pip as follows :

Stable :

pip install git+https://dev.the-phi.com/git/healthcareio/privacykit.git@release

Latest Development (not fully tested):

pip install git+https://dev.the-phi.com/git/healthcareio/privacykit.git@dev

The framework will depend on pandas and numpy (for now). Below is a basic sample to get started quickly.

import numpy as np
import pandas as pd
import privacykit

mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":np.random.choice( np.random.randint(1,10),50),"z":np.random.choice( np.random.randint(1,10),50),"r":np.random.choice( np.random.randint(1,10),50)  })
print (mydf.risk.evaluate())



#
# computing journalist and pitman
#   - Insure the population size is much greater than the sample size 
#   - Insure the fields are identical in both sample and population
#
pop = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),150),"y":np.random.choice( np.random.randint(1,10),150) ,"z":np.random.choice( np.random.randint(1,10),150),"r":np.random.choice( np.random.randint(1,10),150)})
print (mydf.risk.evaluate(pop=pop))

@TODO: - Evaluation of how sparse attributes are (the ratio of non-null over rows) - Have a smart way to drop attributes (based on the above in random policy search) Basic examples that illustrate usage of the the framework are in the notebook folder. The example is derived from