Privacy Tutorial Tasks 19th September

In tomorrow's session, bring your own laptops for implementing the following tasks.

1. Run salary.py with different parameters (number of people, epsilon)
to see what happens. Compare the results by plotting the error.

2. Extend the ideas to credit.py, for multiple features. Upper and lower bounds for each variable should be considered for calculating the laplace noise. Generate data for different values of epsilon and analyse the changes in the accuracy of the classifier. Make a plot for model accuracy as epsilon changes.

https://github.com/olethrosdc/ml-society-science/tree/master/src/privacy