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multi-level privacy protection

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7 - Security and Privacy Issues in Edge Learning Systems
Song Guo, The Hong Kong Polytechnic University, Zhihao Qu, The Hong Kong Polytechnic University
Book:

Edge Learning for Distributed Big Data Analytics

Published online:

14 January 2022

Print publication:

10 February 2022, pp 112-130
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Summary

Conventional distributed machine learning manages the training data in a centralized mode without considering the privacy and security problems during training or inference. With the rapid development and wide deployment of artificial intelligence technology these days, privacy protection has gained more and more attention. Moreover, EL participants usually are small devices (e.g., smartphones, sensors) that have weak defense ability and can be easily compromised under possible attacks. In this chapter, we first introduce a security guarantee mechanism in Edge Learning including the defense methods for data-oriented attacks and model-oriented attacks. Then, we summarize the mainstream methods of privacy protection including differential privacy, secure multi-party computation, and homomorphic encryption. Finally, we discuss future directions in this field.