Secure Metric Learning via Differential Pairwise Privacy

Li, Jing; Pan, Yuangang; Sui, Yulei; Tsang, Ivor W.

doi:10.1109/tifs.2020.2997183

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…In [75], Li et al designed the secure metric learning method based on differential pairwise privacy. In [76], Katewa et al conducted a survey on differential privacy in network identification.…”

Section: B Progress and Developmentmentioning

confidence: 99%

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

Jiang,

Li,

Yue

et al. 2021

Preprint

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The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy.

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Section: B Progress and Developmentmentioning

confidence: 99%