We propose a novel privacy preserving learning algorithm that achieves semi-supervised learning in graphs. In real world networks, such as disease infection ...
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Abstract. We propose a novel privacy preserving learning algorithm that achieves semi-supervised learning in graphs. In real world networks,.
ABSTRACT. This paper proposes a privacy-aware framework for distributed semi- supervised learning. In particular, we consider a semi-supervised.
Privacy Preserving Semi-supervised Learning for Labeled Graphs. H. Arai, and J. Sakuma. ECML/PKDD (1), volume 6911 of Lecture Notes in Computer Science ...
A parameter-masking privacy-preserving Expectation-Maximization (EM) algorithm and a mixture-model-based semi-supervised learning algorithm are proposed as ...
Bibliographic details on Privacy Preserving Semi-supervised Learning for Labeled Graphs.
Privacy Preserving Semi-Supervised Learning for Labeled Graphs ...
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Privacy Preserving Semi-Supervised Learning for Labeled Graphs. H. Arai, and J. Sakuma. Proceedings of the ECML/PKDD 2011, (2011 ) ...
We propose a parameter-masking privacy-preserving Expectation-Maximization (EM) algorithm and a mixture-model-based semi-supervised learning algorithm as the ...
[30] formulate the graph-based semi-supervised learning problem based on a Gaussian random field, by analyzing its intimate connections with random walks and.
Jul 20, 2021 · In this paper, a mixture-model-based solution is proposed for inductive and effective semi-supervised learning in DPPDM. Our motivation lies in ...