May 14, 2024, 4:11 a.m. | Narasimha Raghavan Veeraragavan, Mohammad Hossein Tabatabaei, Severin Elvatun, Vibeke Binz Vallevik, Siri Lar{\o}nningen, Jan F Nyg{\aa}rd

cs.CR updates on

arXiv:2405.07196v1 Announce Type: cross
Abstract: Synthetic data generation is increasingly recognized as a crucial solution to address data related challenges such as scarcity, bias, and privacy concerns. As synthetic data proliferates, the need for a robust evaluation framework to select a synthetic data generator becomes more pressing given the variety of options available. In this research study, we investigate two primary questions: 1) How can we select the most suitable synthetic data generator from a set of options for a …

address arxiv bias blockchain challenges cs.db cs.dc cs.lg data evaluation framework generator options privacy privacy concerns scarcity select solution synthetic synthetic data

Sr. Product Manager

@ MixMode | Remote, US

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

Information Security (Network) Consultant

@ Xcellink Pte Ltd | Singapore, Singapore, Singapore

Information Security Management System Manager

@ Babcock | Bristol, GB, BS3 2HQ