Work

Adaptive Privacy Based on Mutual Information for Machine Learning in Edge–Cloud Environments

Year: 2025

Type: article

Abstract: Edge-cloud computing demands privacy-preserving techniques that balance data utility and computational efficiency. Traditional methods often degrade machine learning (ML) performance by applying uniform noise across all features. This work introduces Mutual Information Adaptive Differential Privacy (MIADP), an adaptive strategy that allocates privacy budgets based on feature importance and applies correlation-aware noise to preserve key statistical relationships. Experiments show that MIADP maintains strong ML performance while ensuring efficiency suitable for edge deployment.

Source: IEEE Networking Letters

DOI: 10.1109/lnet.2025.3607555

Authors: Ivo A. Pimenta, Marcello H. Lee, Luiz F. Bittencourt, Rafael L. Gomes

Institutions: Universidade Estadual do Ceará, Universidade Estadual de Campinas (UNICAMP)

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funder: Conselho Nacional de Desenvolvimento Científico e Tecnológico