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
QA Customer Response Engineer
@ ORBCOMM | Sterling, VA Office, Sterling, VA, US
Enterprise Security Architect
@ Booz Allen Hamilton | USA, TX, San Antonio (3133 General Hudnell Dr) Client Site
DoD SkillBridge - Systems Security Engineer (Active Duty Military Only)
@ Sierra Nevada Corporation | Dayton, OH - OH OD1
Senior Development Security Analyst (REMOTE)
@ Oracle | United States
Software Engineer - Network Security
@ Cloudflare, Inc. | Remote
Software Engineer, Cryptography Services
@ Robinhood | Toronto, ON