all InfoSec news
Metric Differential Privacy at the User-Level
May 7, 2024, 4:11 a.m. | Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri
cs.CR updates on arXiv.org arxiv.org
Abstract: Metric differential privacy (DP) provides heterogeneous privacy guarantees based on a distance between the pair of inputs. It is a widely popular notion of privacy since it captures the natural privacy semantics for many applications (such as, for location data) and results in better utility than standard DP. However, prior work in metric DP has primarily focused on the \textit{item-level} setting where every user only reports a single data item. A more realistic setting is …
applications arxiv cs.cr data differential privacy inputs location location data metric natural notion popular privacy results semantics standard utility
More from arxiv.org / cs.CR updates on arXiv.org
A Privacy Preserving System for Movie Recommendations Using Federated Learning
2 days, 20 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