On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel. (arXiv:2208.01113v2 [cs.CR] UPDATED)

Recent Deep Learning (DL) advancements in solving complex real-world tasks
have led to its widespread adoption in practical applications. However, this
opportunity comes with significant underlying risks, as many of these models
rely on privacy-sensitive data for training in a variety of applications,
making them an overly-exposed threat surface for privacy violations.
Furthermore, the widespread use of cloud-based Machine-Learning-as-a-Service
(MLaaS) for its robust infrastructure support has broadened the threat surface
to include a variety of remote side-channel attacks. In this …

channel networks neural networks privacy user privacy