Posted on March 4, 2020 | Back to Showreel

Privacy-preserving Learning via Deep Net Pruning

Tags: privacy, technical | Paper

This is interesting paper in that it shows how “differential privacy” can be related to network pruning. Differential privacy is the idea that we can hide individual datapoints by adding noise. This is useful if, say, working on medical data where we want to not reveal individual patients.

The trade-off is between how much worse does the network get as we increase the privacy (i.e. make it harder to recover individual datapoints). This paper links this idea with the idea of pruning neural networks in a precise way.