all InfoSec news
Differentially Private Language Models for Secure Data Sharing. (arXiv:2210.13918v1 [cs.LG])
Oct. 26, 2022, 1:24 a.m. | Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, Mrinmaya Sachan
cs.CR updates on arXiv.org arxiv.org
To protect the privacy of individuals whose data is being shared, it is of
high importance to develop methods allowing researchers and companies to
release textual data while providing formal privacy guarantees to its
originators. In the field of NLP, substantial efforts have been directed at
building mechanisms following the framework of local differential privacy,
thereby anonymizing individual text samples before releasing them. In practice,
these approaches are often dissatisfying in terms of the quality of their
output language due …
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
Security Officer Hospital - Major Hospital Account - Full-Time - Healthcare Security
@ Allied Universal | Anaheim, CA, United States
Product Security Lead
@ Lely | Maassluis, Netherlands
Summer Associate, IT Information Security (Temporary)
@ Vir Biotechnology, Inc. | San Francisco, California, United States
Director, Governance, Risk and Compliance - Corporate
@ Ryan Specialty | Chicago, IL, US, 60606
Cybersecurity Governance, Risk, and Compliance Engineer
@ Emerson | Shakopee, MN, United States