updated readme

README.md

@@ -1,10 +1,56 @@
-# deid-risk
+# Re-Identification Risk
 
-The code below extends a data-frame by adding it the ability to compute de-identification risk (marketer, prosecutor).
+This framework computes re-identification risk of a dataset assuming the data being shared can be loaded into a dataframe (pandas)
-Because data-frames can connect to any database/file it will be the responsibility of the user to load the dataset into a data-frame.
+The framework will compute the following risk measures:
+    - marketer
+    - prosecutor
+    - pitman
+
+References :
 
-Basic examples that illustrate usage of the the framework are in the notebook folder. The example is derived from 
     [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)
+
+This framework integrates pandas (for now) as an extension and can be used in two modes :
+* 1. explore: *
+    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 it can come up with
+
+* 2. 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
+
+# Usage:
+
+    import numpy as np
+    import pandas as pd
+    from pandas_risk import *
+
+    mydf = pd.DataFrame({"x":np.random.choice( np.random.randint(1,10),50),"y":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) ,"q":np.random.choice( np.random.randint(1,10),150)})
+    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 
+
 
 Dependencies:
 	numpy