Day 16: Differential privacy for deep learning
Today, I learned from this discussion,
Sensitivity is depending on the query. It is a distance between full database(size = n) with a parallel databases ( n database with @ size = n-1). For example, when the query is sum, then sensitivity = 1. Difference if we remove one entry in database is 1 since the database consists only 0 and 1. While for mean query, the sensitivity will be 1/len(db).
@Karan Khishinani sent me the definitions about sensitivity and epsilon:
My activity continue on watching lessons 6 concepts 1, 2, 3 and interviews on 7 Differential Privacy at Apple.
Next will be watching webinar. While waiting. I am browsing the fast.ai website.
Sensitivity is depending on the query. It is a distance between full database(size = n) with a parallel databases ( n database with @ size = n-1). For example, when the query is sum, then sensitivity = 1. Difference if we remove one entry in database is 1 since the database consists only 0 and 1. While for mean query, the sensitivity will be 1/len(db).
@Karan Khishinani sent me the definitions about sensitivity and epsilon:
My activity continue on watching lessons 6 concepts 1, 2, 3 and interviews on 7 Differential Privacy at Apple.
Next will be watching webinar. While waiting. I am browsing the fast.ai website.
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