all AI news
Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM. (arXiv:2206.03488v1 [cs.CR])
June 9, 2022, 1:10 a.m. | Yuzhe Li, Yong Liu, Bo Li, Weiping Wang, Nan Liu
cs.LG updates on arXiv.org arxiv.org
In this paper, we focus our attention on private Empirical Risk Minimization
(ERM), which is one of the most commonly used data analysis method. We take the
first step towards solving the above problem by theoretically exploring the
effect of epsilon (the parameter of differential privacy that determines the
strength of privacy guarantee) on utility of the learning model. We trace the
change of utility with modification of epsilon and reveal an established
relationship between epsilon and utility. We then …
analysis arxiv data data analysis differential privacy erm privacy understanding
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Business Intelligence Architect - Specialist
@ Eastman | Hyderabad, IN, 500 008