537 research outputs found
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, 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.This work is partially supported by the Generalitat de Catalunya under grant 2017 SGR 962 and the HORIZON-GPHOENIX (101070586) and HORIZON-EUVITAMIN-V (101093062) projects.Peer ReviewedPostprint (published version
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
The advent of federated learning has facilitated large-scale data exchange
amongst machine learning models while maintaining privacy. Despite its brief
history, federated learning is rapidly evolving to make wider use more
practical. One of the most significant advancements in this domain is the
incorporation of transfer learning into federated learning, which overcomes
fundamental constraints of primary federated learning, particularly in terms of
security. This chapter performs a comprehensive survey on the intersection of
federated and transfer learning from a security point of view. The main goal of
this study is to uncover potential vulnerabilities and defense mechanisms that
might compromise the privacy and performance of systems that use federated and
transfer learning.Comment: Accepted for publication in edited book titled "Federated and
Transfer Learning", Springer, Cha
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