Get started
Work

A Performance-Preserving Scheme for Classifying Differentially Private Remote Sensing Images

Year: 2025
Type: conference-paper
Abstract: Privacy preservation of remote sensing images is often necessary to effectively balance data utilization and sensitive information conservation. Differential privacy (DP) offers a rigorous framework for privacy preservation and it is widely adopted in various domains. However, in the domain of remote sensing, effective privacy-preservation of input images using DP method is mostly achieved with a cost of significantly degraded performance in downstream data mining tasks, such as classification. We address this issue by proposing a performance-preserving DP-based input perturbation approach, termed as P2DP. Typically, P2DP employs a controlled noise-based shape obscurity scheme so as to confound adversaries attempting to decipher the precise details from remote sensing images. Experimentation on benchmark UCMerced and WHU-RS19 datasets validates the efficacy of the proposed P2DP approach. Our research contribution collectively advances the state of the art in privacy-preserving techniques for remote sensing images, offering a practical and customizable solution for balancing privacy concerns with the need for accurate and informative data analysis.
DOI: 10.1016/j.procs.2025.03.256
Authors: Sanjib Chowdhury, Monidipa Das
Language: English
Open Access status: diamond
OpenAlex ID: W4410775676
FWCI:
Cites: 10
Cited by:
Related to: 10