A Genetic Attack Against Machine Learning Classifiers to Steal Biometric Actigraphy Profiles from Health Related Sensor Data

In this work, we propose the use of a genetic-algorithm-based attack against machine learning classifiers with the aim of ‘stealing’ users’ biometric actigraphy profiles from health related sensor data. The target classification model uses daily actigraphy patterns for user identification. The biome...

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Detalles Bibliográficos
Autores principales: Garcia-Ceja, Enrique, Morin, Brice, Aguilar-Rivera, Anton, Riegler, Michael Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Image & Signal Processing
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497442/
https://www.ncbi.nlm.nih.gov/pubmed/32929615
http://dx.doi.org/10.1007/s10916-020-01646-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497442/
https://www.ncbi.nlm.nih.gov/pubmed/32929615
http://dx.doi.org/10.1007/s10916-020-01646-y

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