  We explore the problem of selectively forgetting a particular subset of the
data used for training a deep neural network. While the effects of the data to
be forgotten can be hidden from the output of the network, insights may still
be gleaned by probing deep into its weights. We propose a method for
"scrubbing'" the weights clean of information about a particular set of
training data. The method does not require retraining from scratch, nor access
to the data originally used for training. Instead, the weights are modified so
that any probing function of the weights is indistinguishable from the same
function applied to the weights of a network trained without the data to be
forgotten. This condition is a generalized and weaker form of Differential
Privacy. Exploiting ideas related to the stability of stochastic gradient
descent, we introduce an upper-bound on the amount of information remaining in
the weights, which can be estimated efficiently even for deep neural networks.
