Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation. (arXiv:2309.08190v1 [cs.CR])

Over the past few years, a tremendous growth of machine learning was brought
about by a significant increase in adoption and implementation of cloud-based
services. As a result, various solutions have been proposed in which the
machine learning models run on a remote cloud provider and not locally on a
user's machine. However, when such a model is deployed on an untrusted cloud
provider, it is of vital importance that the users' privacy is preserved. To
this end, we propose …

adoption cloud cloud-based cloud provider dark function growth implementation locally machine machine learning machine learning models privacy result run services solutions