A survey of machine and deep learning methods for privacy protection in the Internet of things
Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, aut...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/385085 |
| Acceso en línea: | https://hdl.handle.net/2117/385085 https://dx.doi.org/10.3390/s23031252 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning Machine learning Data protection Internet of things Cybersecurity IoT networks Privacy Aprenentatge profund Aprenentatge automàtic Protecció de dades Internet de les coses Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | Recent advances in hardware and information technology have accelerated the proliferation of smart and interconnected devices facilitating the rapid development of the Internet of Things (IoT). IoT applications and services are widely adopted in environments such as smart cities, smart industry, autonomous vehicles, and eHealth. As such, IoT devices are ubiquitously connected, transferring sensitive and personal data without requiring human interaction. Consequently, it is crucial to preserve data privacy. This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT. First, we present an in depth analysis of current privacy threats and attacks. Then, for each ML architecture proposed, we present the implementations, details, and the published results. Finally, we identify the most effective solutions for the different threats and attacks. |
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