Cargando…
Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks
BACKGROUND: Although evidence supporting the feasibility of large-scale mobile health (mHealth) systems continues to grow, privacy protection remains an important implementation challenge. The potential scale of publicly available mHealth applications and the sensitive nature of the data involved wi...
Autores principales: | Shen, Alexander, Francisco, Luke, Sen, Srijan, Tewari, Ambuj |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160928/ https://www.ncbi.nlm.nih.gov/pubmed/37079370 http://dx.doi.org/10.2196/43664 |
Ejemplares similares
-
Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data
por: Liu, Jessica Chia, et al.
Publicado: (2021) -
mHealth Systems Need a Privacy-by-Design Approach: Commentary on “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review”
por: Tewari, Ambuj
Publicado: (2023) -
Federated Learning for Privacy-Aware Human Mobility Modeling
por: Ezequiel, Castro Elizondo Jose, et al.
Publicado: (2022) -
Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation
por: Yu, Yun William, et al.
Publicado: (2020) -
A Privacy Attack on Multiple Dynamic Match-key based Privacy-Preserving Record Linkage
por: Vidanage, A, et al.
Publicado: (2020)