A Privacy-Preserving Semisupervised Algorithm Under Maximum Correntropy Criterion

Ling Zuo

doi:10.1109/TNNLS.2021.3083535

A Privacy-Preserving Semisupervised Algorithm Under Maximum Correntropy Criterion

2022-11-01

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Kim‐Kwang Raymond Choo

DOI：10.1109/TNNLS.2021.3083535

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Existing semisupervised learning approaches generally focus on the single-agent (centralized) setting, and hence, there is the risk of privacy leakage during joint data processing. At the same time, using the mean square error criterion in such approaches does not allow one to efficiently deal with problems involving non-Gaussian distribution. Thus, in this article, we present a novel privacy-preserving semisupervised algorithm under the maximum correntropy criterion (MCC). The proposed algorithm allows us to share data among different entities while effectively mitigating the risk of privacy leaks. In addition, under MCC, our proposed approach works well for data with non-Gaussian distribution noise. Our experiments on three different learning tasks demonstrate that our method distinctively outperforms the related algorithms in common regression learning scenarios.

Keywords:

Semisupervised learning

Manifolds

Data privacy

Kernel

Privacy

Laplace equations

Prediction algorithms

Correntropy

distributed semisupervised learning

excess generalization error

privacy preserving

A Privacy-Preserving Semisupervised Algorithm Under Maximum Correntropy Criterion

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