all InfoSec news
Stochastic Gradient Langevin Unlearning
March 27, 2024, 4:11 a.m. | Eli Chien, Haoyu Wang, Ziang Chen, Pan Li
cs.CR updates on arXiv.org arxiv.org
Abstract: ``The right to be forgotten'' ensured by laws for user data privacy becomes increasingly important. Machine unlearning aims to efficiently remove the effect of certain data points on the trained model parameters so that it can be approximately the same as if one retrains the model from scratch. This work proposes stochastic gradient Langevin unlearning, the first unlearning framework based on noisy stochastic gradient descent (SGD) with privacy guarantees for approximate unlearning problems under convexity …
arxiv can cs.cr cs.lg data data points data privacy effect important laws machine points privacy remove right to be forgotten user data work
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Information Security Engineers
@ D. E. Shaw Research | New York City
Technology Security Analyst
@ Halton Region | Oakville, Ontario, Canada
Senior Cyber Security Analyst
@ Valley Water | San Jose, CA
Sr. Staff Firmware Engineer – Networking & Firewall
@ Axiado | Bengaluru, India
Compliance Architect / Product Security Sr. Engineer/Expert (f/m/d)
@ SAP | Walldorf, DE, 69190
SAP Security Administrator
@ FARO Technologies | EMEA-Portugal