Title: Better accuracy with quantified privacy: representations learned via reconstructive adversarial network,
Abstract: The remarkable success of machine learning, especially deep learning, has
produced a variety of cloud-based services for mobile users. Such services
require an end user to send data to the service provider, which presents a
serious challenge to end-user privacy. To address this concern, prior works
either add noise to the data or send features extracted from the raw data. They
struggle to balance between the utility and privacy because added noise reduces
utility and raw data can be reconstructed from extracted features. This work
represents a methodical departure from prior works: we balance between a
measure of privacy and another of utility by leveraging adversarial learning to
find a sweeter tradeoff. We design an encoder that optimizes against the
reconstruction error (a measure of privacy), adversarially by a Decoder, and
the inference accuracy (a measure of utility) by a Classifier. The result is
RAN, a novel deep model with a new training algorithm that automatically
extracts features for classification that are both private and useful. It turns
out that adversarially forcing the extracted features to only conveys the
intended information required by classification leads to an implicit
regularization leading to better classification accuracy than the original
model which completely ignores privacy. Thus, we achieve better privacy with
better utility, a surprising possibility in machine learning! We conducted
extensive experiments on five popular datasets over four training schemes, and
demonstrate the superiority of RAN compared with existing alternatives.