Differential Privacy
Differential Privacy is a rigorous mathematical definition and set of techniques that allow for statistical analyses of databases while providing strong guarantees about the privacy of individual data points. It quantifies and limits the influence any single individual's data can have on the output, typically by introducing calibrated noise, making it difficult to infer information about specific people.
It's like adding a pinch of salt to a large batch of soup; you can still taste the soup, but it's impossible to tell if one specific grain of salt was added or not.
Differential Privacy enables organizations to unlock insights from sensitive data for research or product improvements while protecting individual user privacy, fostering trust and ethical AI development.
Learn one new AI thing every day.
Daily Deck sends you seven plain-English cards like this every morning. Free.
Start free