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steve 2019-03-05 12:23:35 -06:00
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# Re-Identification Risk # Re-Identification Risk
This framework computes re-identification risk of a dataset by extending pandas. It works like a pandas **add-on** 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. 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](https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf)
There are two modes available : There are two modes available :
**explore:** **explore:**
Here the assumption is that we are not sure of the attributes to be disclosed, 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.
The framework will explore a variety of combinations and associate risk measures every random combinations it can come up with
**evaluation** **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. 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 : ### Four risk measures are computed :
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### Usage: ### Usage:
The framework will depend on pandas and numpy (for now) The framework will depend on pandas and numpy (for now). Below is a basic sample to get started quickly.
import numpy as np import numpy as np
import pandas as pd import pandas as pd
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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)}) 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)})
mydf.risk.evaluate(pop=pop) mydf.risk.evaluate(pop=pop)
### References :
[http://ehelthinformation.ca] (http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf)
[https://www.scb.se/contentassets](https://www.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/applying-pitmans-sampling-formula-to-microdata-disclosure-risk-assessment.pdf)
@TODO: @TODO:
- Evaluation of how sparse attributes are (the ratio of non-null over rows) - Evaluation of how sparse attributes are (the ratio of non-null over rows)