Confidentiality-preserving Machine Learning Scheme to Detect Soft-failures in Optical Communication Networks

被引：0

作者：

Silva, Moises Felipe ^{[1
]}

Pacini, Alessandro ^{[2
]}

Sgambelluri, Andrea ^{[2
]}

Paolucci, Francesco ^{[3
]}

Valcarenghi, Luca ^{[2
]}

机构：

[1] Los Alamos Natl Lab, Engn Inst, Los Alamos, NM 87545 USA

[2] Scuola Super Sant Anna, TeCIP Inst, Pisa, Italy

[3] CNIT, Pisa, Italy

来源：

2022 EUROPEAN CONFERENCE ON OPTICAL COMMUNICATION (ECOC) | 2022年

基金：

欧盟地平线“2020”;

关键词：

D O I：

暂无

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

We introduce a third-party confidentiality-preserving machine learning scheme for soft-failure detection leveraging the robustness of the principal components algorithm to the changes in the rotation of the data axis. We demonstrate that random scrambling of the data is effective to hide sensitive telemetry information. (C) 2022 The Author(s)

引用

页数：4

共 22 条

[1] Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks
Silva, Moises Felipe
Sgambelluri, Andrea
Pacini, Alessandro
Paolucci, Francesco
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Valcarenghi, Luca
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[8] Machine Learning-Based Communication Failure Identification Scheme for Directional Industrial IoT Networks
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[J]. IEEE SYSTEMS JOURNAL, 2023, 17 (01): : 1559 - 1568
[9] Free-space optical communication assisted emergency communication scheme against multiple fiber link failures in low-edge-connected optical networks
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[J]. TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2023, 34 (03)
[10] Machine learning framework for timely soft-failure detection and localization in elastic optical networks
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[J]. JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING, 2023, 15 (10) : E74 - E85

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