all InfoSec news
Bayesian Estimation of Differential Privacy. (arXiv:2206.05199v1 [cs.LG])
June 13, 2022, 1:20 a.m. | Santiago Zanella-Béguelin (Microsoft Research), Lukas Wutschitz (Microsoft), Shruti Tople (Microsoft Research), Ahmed Salem (Microsoft Research),
cs.CR updates on arXiv.org arxiv.org
Algorithms such as Differentially Private SGD enable training machine
learning models with formal privacy guarantees. However, there is a discrepancy
between the protection that such algorithms guarantee in theory and the
protection they afford in practice. An emerging strand of work empirically
estimates the protection afforded by differentially private training as a
confidence interval for the privacy budget $\varepsilon$ spent on training a
model. Existing approaches derive confidence intervals for $\varepsilon$ from
confidence intervals for the false positive and false …
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
SOC 2 Manager, Audit and Certification
@ Deloitte | US and CA Multiple Locations
Level 1 SOC Analyst
@ Telefonica Tech | Dublin, Ireland
Specialist, Database Security
@ OP Financial Group | Helsinki, FI
Senior Manager, Cyber Offensive Security
@ Edwards Lifesciences | Poland-Remote
Information System Security Officer
@ Booz Allen Hamilton | USA, AL, Huntsville (4200 Rideout Rd SW)
Senior Security Analyst - Protective Security (Open to remote across ANZ)
@ Canva | Sydney, Australia