Web: http://arxiv.org/abs/2211.11896

Nov. 23, 2022, 2:20 a.m. | Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash Varadarajan, Chiyuan Zhang

cs.CR updates on arXiv.org arxiv.org

A well-known algorithm in privacy-preserving ML is differentially private
stochastic gradient descent (DP-SGD). While this algorithm has been evaluated
on text and image data, it has not been previously applied to ads data, which
are notorious for their high class imbalance and sparse gradient updates. In
this work we apply DP-SGD to several ad modeling tasks including predicting
click-through rates, conversion rates, and number of conversion events, and
evaluate their privacy-utility trade-off on real-world datasets. Our work is
the first …


Senior Cloud Security Engineer

@ HelloFresh | Berlin, Germany

Senior Security Engineer

@ Reverb | Remote, US

I.S. Security Analyst

@ YVFWC | Yakima, WA

Snr Professional Services Consultant - XSIAM

@ Palo Alto Networks | Madrid, Spain

Data Governor and Security Specialist

@ Dynatrace | Milan, Italy

Principal Windows Exploit Security Researcher (Cortex XDR)

@ Palo Alto Networks | Tel Aviv-Yafo, Israel

Information System Security Officer (ISSO)

@ SciTec | Boulder, Colorado, United States

Application Security Design Architect

@ Fivesky | Alpharetta, GA

Product Cybersecurity Lead

@ SciTec | Boulder, Colorado, United States

Cybersecurity Consultant

@ Sia Partners | Rotterdam, Netherlands

Senior Cybersecurity Engineer

@ Visa | Austin, TX, United States

Manager Pentest H/F

@ Hifield | Sèvres, France