Zobacz książki o podobnej tematyce

Więcej informacji o Privacy Preservation in IoT: Machine Learning Approaches

• Dane szczegółowe
• Opis
• Ulubione w innej kategorii

Za ten zakup dostaniesz 167 punkty

Opis

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.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.Readers may trace timely contributions on machine learning-driven privacy preservation in IoTs. The advances cover different applications, such as cyber-physical systems, fog computing, and location-based services. This book will be of interest to forthcoming scientists, policymakers, researchers, and postgraduates.

Szczegóły książki

Kategoria Książki po angielsku Computing & information technology Computer security Privacy & data protection

• Pełny tytuł: Privacy Preservation in IoT: Machine Learning Approaches
• Podtytuł: A Comprehensive Survey and Use Cases
• Autor: Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang
• Język: Angielski
• Oprawa: Miękka
• Liczba stron: 119
• EAN: 9789811917967
• ID: 38813590
• Wydawca: Springer Verlag, Singapore
• Waga: 215 g
• Wymiary: 235 × 155 × 8 mm
• Data wydania: 28. April 2022

---

---

---