Characterizing the Sample Complexity of Private Learners: Kobbi Nissim, Ben Gurion University
- Starts: 11:00 am on Wednesday, December 5, 2012
- Ends: 12:00 pm on Wednesday, December 5, 2012
The notion of private learning [Kasiviswanathan, Lee, N, Raskhodnikova, and
Smith '08] is a combination of PAC (probably approximately correct) learning
[Valiant 84] and differential privacy [Dwork, McSherry, N, and Smith '06].
Kasiviswanathan el al. presented a generic construction of private learner
for finite concept classes, where the sample complexity depends
logarithmically in the size of the concept class. For concept classes of
small VC dimension, this sample complexity is significantly larger than what
is sufficient for non-private learning.
In this talk I will motivate differential privacy and private learning, and
will present some of the known bounds on the sample complexity of private
learners. In particular, a recent characterization of the sample complexity
as a combinatorial measure of the learned concept class.
Joint work with Amos Beimel and Uri Stemmer.
- Location:
- MCS 148