Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Privacy Preservation in Iot: Machine Learning Approaches: A Comprehensive Survey and Use Cases

Privacy Preservation in Iot: Machine Learning Approaches: A Comprehensive Survey and Use Cases

Qu, Youyang
Gao, Longxiang
Yu, Shui

Paperback

Series: Springerbriefs in Computer Science

Technology & EngineeringGeneral ComputersComputer Security

ISBN10: 9811917965
ISBN13: 9789811917967
Publisher: Springer Nature
Published: Apr 28 2022
Pages: 119
Weight: 0.43
Height: 0.28 Width: 6.14 Depth: 9.21
Language: English

This book aims to sort out the clear logic of the development of machine learning-driven privacy preservation in IoTs, including the advantages and disadvantages, as well as the future directions in this under-explored domain. In big data era, an increasingly massive volume of data is generated and transmitted in Internet of Things (IoTs), which poses great threats to privacy protection. Motivated by this, an emerging research topic, machine learning-driven privacy preservation, is fast booming to address various and diverse demands of IoTs. However, there is no existing literature discussion on this topic in a systematically manner.

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