Hello, in this lesson you will learn
how to explore differential privacy. Data science projects,
including machine learning projects, involve analysis of data and often that
data includes sensitive personal details that should be kept private. In practice, most reports that
are published from the data include aggregations of the data, which you
may think would provide some privacy. After all, the aggregated results do
not reveal the individual data values. However, consider a case where multiple
analysis of the data result in reported aggregations, that when combined, could be used to work out information
about individuals in the source data set. Suppose ten participants share
data about their location and salary from which to reports are produced. An aggregated salary report that tells
us the average salaries in New York, San Francisco, and Seattle. A worker location report that tells us
that 10% of the study participants. In other words,
a single person is based in Seattle. From these two reports, we can easily determine the specific
salary of the Seattle-based participant. Anyone reviewing both studies who happens
to know a person from Seattle that participated, now knows
that person's salary. In this lesson, you'll explore
differential privacy, a technique that can help protect an individual's
data against this kind of exposure. More specifically, you will learn how to
articulate the problem of data privacy, describe how differential privacy works,
configure parameters for differential privacy, and perform
deferentially private data analysis