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Privacy Preservation in IoT: Machine Learning Approaches

A Comprehensive Survey and Use Cases

Produktform: Buch / Einband - flex.(Paperback)

The issues of existing privacy protection methods (differential privacy, clustering, anonymity, etc.) for IoTs, such as low data utility, high communication overload, and unbalanced trade-off, are identified to the necessity of machine learning-driven privacy preservation. Besides, the leading and emerging attacks pose further threats to privacy protection in this scenario. To mitigate the negative impact, machine learning-driven privacy preservation methods for IoTs are discussed in detail on both the advantages and flaws, which is followed by potentially promising research directions. weiterlesen

Dieser Artikel gehört zu den folgenden Serien

Sprache(n): Englisch

ISBN: 978-9811917967 / 978-9811917967 / 9789811917967

Verlag: Springer Singapore

Erscheinungsdatum: 28.04.2022

Seiten: 119

Auflage: 1

Autor(en): Longxiang Gao, Shui Yu, Yong Xiang, Youyang Qu

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