all InfoSec news
Privacy Enhancement for Cloud-Based Few-Shot Learning. (arXiv:2205.07864v2 [cs.LG] UPDATED)
Aug. 24, 2022, 1:20 a.m. | Archit Parnami, Muhammad Usama, Liyue Fan, Minwoo Lee
cs.CR updates on arXiv.org arxiv.org
Requiring less data for accurate models, few-shot learning has shown
robustness and generality in many application domains. However, deploying
few-shot models in untrusted environments may inflict privacy concerns, e.g.,
attacks or adversaries that may breach the privacy of user-supplied data. This
paper studies the privacy enhancement for the few-shot learning in an untrusted
environment, e.g., the cloud, by establishing a novel privacy-preserved
embedding space that preserves the privacy of data and maintains the accuracy
of the model. We examine the …
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Social Engineer For Reverse Engineering Exploit Study
@ Independent study | Remote
Premium Hub - CoE: Business Process Senior Consultant, SAP Security Role and Authorisations & GRC
@ SAP | Dublin 24, IE, D24WA02
Product Security Response Engineer
@ Intel | CRI - Belen, Heredia
Application Security Architect
@ Uni Systems | Brussels, Brussels, Belgium
Sr Product Security Engineer
@ ServiceNow | Hyderabad, India
Analyst, Cybersecurity & Technology (Initial Application Deadline May 20th, Final Deadline May 31st)
@ FiscalNote | United Kingdom (UK)