all InfoSec news
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. (arXiv:2206.05091v2 [cs.CR] UPDATED)
Oct. 21, 2022, 1:24 a.m. | Edwige Cyffers, Mathieu Even, Aurélien Bellet, Laurent Massoulié
cs.CR updates on arXiv.org arxiv.org
Decentralized optimization is increasingly popular in machine learning for
its scalability and efficiency. Intuitively, it should also provide better
privacy guarantees, as nodes only observe the messages sent by their neighbors
in the network graph. But formalizing and quantifying this gain is challenging:
existing results are typically limited to Local Differential Privacy (LDP)
guarantees that overlook the advantages of decentralization. In this work, we
introduce pairwise network differential privacy, a relaxation of LDP that
captures the fact that the privacy …
amplification decentralized optimization peer-to-peer privacy
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Information Security Engineers
@ D. E. Shaw Research | New York City
Database Security Engineer, Assistant Vice President
@ MUFG | Tampa - 4050 West Boy Scout Blvd.
Senior IR & SecOps Engineer
@ JFrog | Tel Aviv
Consultant ITSCM / IT-Notfallmanagement (m/w/d)
@ Schwarz Gruppe | Berlin, DE
Freelancer Auditor Information Security - ISO 27001 - Netherlands
@ LRQA | Rotterdam, NL
GG9b-Assoc Eng II, Services
@ HARMAN International | IN Bengaluru EOIZ Indust Area Campus HCS