Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach. (arXiv:2309.04427v1 [cs.LG])

Several domains increasingly rely on machine learning in their applications.
The resulting heavy dependence on data has led to the emergence of various laws
and regulations around data ethics and privacy and growing awareness of the
need for privacy-preserving machine learning (ppML). Current ppML techniques
utilize methods that are either purely based on cryptography, such as
homomorphic encryption, or that introduce noise into the input, such as
differential privacy. The main criticism given to those techniques is the fact
that …

applications awareness current data data ethics domains ethics laws laws and regulations led machine machine learning privacy regulations representation techniques