all InfoSec news
Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimes
April 30, 2024, 4:11 a.m. | Victor S. Portella, Nick Harvey
cs.CR updates on arXiv.org arxiv.org
Abstract: We prove lower bounds on the number of samples needed to privately estimate the covariance matrix of a Gaussian distribution. Our bounds match existing upper bounds in the widest known setting of parameters. Our analysis relies on the Stein-Haff identity, an extension of the classical Stein's identity used in previous fingerprinting lemma arguments.
analysis arxiv cs.cr cs.ds cs.lg distribution identity matrix parameter private privately prove stat.ml under
More from arxiv.org / cs.CR updates on arXiv.org
A Privacy Preserving System for Movie Recommendations Using Federated Learning
2 days, 23 hours ago |
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
Computer and Forensics Investigator
@ ManTech | 221BQ - Cstmr Site,Springfield,VA
Senior Security Analyst
@ Oracle | United States
Associate Vulnerability Management Specialist
@ Diebold Nixdorf | Hyderabad, Telangana, India