all AI news
Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning
April 10, 2024, 4:45 a.m. | Fabian Perez, Jhon Lopez, Henry Arguello
cs.CV updates on arXiv.org arxiv.org
Abstract: In the era of cloud computing and data-driven applications, it is crucial to protect sensitive information to maintain data privacy, ensuring truly reliable systems. As a result, preserving privacy in deep learning systems has become a critical concern. Existing methods for privacy preservation rely on image encryption or perceptual transformation approaches. However, they often suffer from reduced task performance and high computational costs. To address these challenges, we propose a novel Privacy-Preserving framework that uses …
abstract applications arxiv become cloud cloud computing computing cs.cr cs.cv data data-driven data privacy deep learning eess.iv information learning systems operators preservation privacy protect systems type
More from arxiv.org / cs.CV updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Research Scientist (Computer Science)
@ Nanyang Technological University | NTU Main Campus, Singapore
Intern - Sales Data Management
@ Deliveroo | Dubai, UAE (Main Office)