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Published in:
2022 | OriginalPaper | Chapter
7. Summary and Outlook
Authors : Youyang Qu, Longxiang Gao, Shui Yu, Yong Xiang
Published in: Privacy Preservation in IoT: Machine Learning Approaches
Publisher: Springer Nature Singapore
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Abstract
In this monograph, we summarize the state-of-art research on machine learning driven privacy protection models in IoT scenarios. As far as we can see, machine learning driven privacy protection is still in its early stage. What have been present in this monograph, like models, theories, and potentially conceptual designs, could serve as a point of departure for follow-up readers, students, engineers, and researchers to investigate this emerging field. Our target is to provide a systematic summary of existing research and application outputs on machine learning driven privacy protection in IoTs. Based on this, we analyze the theoretical and practical applicability under big data settings. Subsequently, we offer the interested readers several future research directions, which we hope the following explorers find them insightful or inspiring from a certain angle.