What is Differential Privacy?

How do we ensure we have valuable data while protecting individuals’ privacy? In a data-driven world, we need to make good decisions about how we analyze data while protecting personally identifiable information (PII). Differential privacy allows us to do that. NIST just completed a Differential Privacy Synthetic Data Challenge run by our Public Safety Communications Research program. Q&A with Mary Theofanos: www.nist.gov/blogs/taking-mea...
More info: www.nist.gov/communications-t...

[@NIST]

Q&A with Mary Theofanos: www.nist.gov/blogs/taking-measure/differential-privacy-qa-nists-mary-theofanos

[@SebastianHernandez-gd3br]

This is a non-dystopian vision of data driven policy response. I love it, feels like an efficient 21st century solution

[@NIST]

Thank you for your comment.

[@andrewbrown6462]

This is really great, especially in light of the HIPAA changes recently announced related to COVID-19 response. Putting data together in such a way that it protects the individual and helps leaders make decisions is critical.

[@NIST]

Thanks for checking out our videos. Please add your comments and let us know what you think. We will be reviewing and then posting comments as long as they are on topic, respectful and do not promote specific products or service.

[@arashakbari6986]

This video is so underrated

[@eldoprano]

What a fine video!

[@macknightxu2199]

for epsilon differential privacy, is it 100% safe for one data point? In what situation a data point is unsafe while satisfying the definition of Differential privacy? Or does it mean that if a function M satisfies the definition of DP, then this function M is always regarded as getting the best privacy protection percentage for input data which is 50% for guessing whether one data point is in the dataset or not?