New Tutorial: Demonstrate DP on DataWarehouse for #300 #346
Conversation
|
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Koukyosyumei
changed the title
run-clang-format.py
Outdated
| raise | ||
| except Exception as e: | ||
| raise UnexpectedError("{}: {}: {}".format(file, e.__class__.__name__, e), e) | ||
| raise UnexpectedError("{}: {}: {}".format( |
There was a problem hiding this comment.
Can we use f-strings here?
There was a problem hiding this comment.
It's not what I intended, and I reset the commit
|
|
||
|
|
||
| expect_near = lambda expected, actual, tol: ( | ||
| def expect_near(expected, actual, tol): return ( |
There was a problem hiding this comment.
is there a reason to move from lambda to this new implementation?
There was a problem hiding this comment.
It's not what I intended, and I reset the commit
Koukyosyumei
force-pushed
the
feature_300
branch
from
990dd08 to
0d7e041
Compare
|
@chinmayshah99 |
chinmayshah99
added
Type: New Feature ➕
chinmayshah99
added
Status: Stale 🍞
2 participants
Description
This PR aims to close #300.
Although the label of #300 is "status in progress", it seems that no one is working on this issue.
This notebook consists of two parts. The first part shows how differential privacy works under the assumption that the attacker already knows almost all of the data except one record. In the second one, the attacker tries to identify one person, and the defender uses differential privacy to avoid this.
It is the first PR for me to this excellent project, so if I misunderstand something, please correct me!
Affected Dependencies
no
How has this been tested?
jupyter lab
Checklist