all InfoSec news
Personalized Privacy Auditing and Optimization at Test Time. (arXiv:2302.00077v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
A number of learning models used in consequential domains, such as to assist
in legal, banking, hiring, and healthcare decisions, make use of potentially
sensitive users' information to carry out inference. Further, the complete set
of features is typically required to perform inference. This not only poses
severe privacy risks for the individuals using the learning systems, but also
requires companies and organizations massive human efforts to verify the
correctness of the released information.
This paper asks whether it is …
auditing banking companies correctness domains features healthcare hiring human information legal optimization organizations privacy privacy risks risks systems test verify