[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123689-en":3,"doc-seo-123689-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123689,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Confidentiality-preserving Machine Learning Algorithms for Soft-failure Detection in Optical Communication Networks","Automated fault management in optical communication networks drives the need for programmable, software-driven architectures and ETSI zero-touch network and service management. As telemetry volume grows and cloud-based services become common, confidentiality and safety risks arise. The work presents a feature-scrambling approach that enables third-party services to operate without direct access to full telemetry content and evaluates four unsupervised algorithms for soft-failure detection on the scrambled data.","| Research Article | Journal of Optical Communications and Networking 1 |\n| --- | --- |\n\nConfidentiality-preserving Machine Learning Algorithms for Soft-failure Detection in Optical Communication Networks  \nMOISES FELIPE SILVA1,* , ANDREA SGAMBELLURI2 , ALESSANDRO PACINI2 , FRANCESCO PAOLUCCI3 , ANDRE GREEN1 , DAVID MASCARENAS1 , AND LUCA VALCARENGHI2  \n1 Los Alamos National Laboratory, Los Alamos, USA  \n2 Scuola Superore Sant’Anna, Pisa, Italy  \n3 CNIT, Pisa, Italy  \n*  \n[Corresponding author: mfelipe@lanl.gov](Corresponding author: mfelipe@lanl.gov)  \nCompiled January 19, 2025  \nAutomated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and softwaredriven architectures to support the operation of agile and self-managed systems. In these scenarios the ETSI zero-touch network and service management (ZSM) approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.  \n© 2025 Optical Society of America  \n[http://dx.doi.org/10.1364/ao.XX.XXXXXX](http://dx.doi.org/10.1364/ao.XX.XXXXXX)  \n1. INTRODUCTION  \nCommunication networks, including optical networks, are currently designed with the aim to provide open and online monitoring data processing and analysis. Software defined networking (SDN) is constantly evolving to support autonomous optical communication networks in which network awareness is implemented in a closed loop fashion, paving the way for Zero Touch Networking [1] . Optical telemetry is a hot topic in the context of disaggregated optical networks [2, 3], thanks to the availability of open data models allowing the exchange of status information between optical node components belonging to different vendors [4] and centralized controllers and monitor handlers. Such platforms facilitate the introduction of machine learning  \n(ML) engines to process online optical data in the data lake.  \nWith the ultimate goal to enable large and fully automated networks, capable of performing policy-driven self-diagnostics [5, 6], most works leverage the advantages of using supervised learning strategies to a variety of goals, ranging from management to forecasting and event detection [7–9] . Among the plethora of approaches, the ones based on artificial neural networks (ANNs) are undoubtedly the most prominent [10–12] .  \nIn this rapid evolving ecosystem, the collection and transfer of the enormous volume of telemetry data brings concerns on the data security, privacy and confidentiality-preservation [13–15] . Particularly for centralized network scenarios, involving the centralization of intelligent data processing modules in a third-party cloud component, preserving the data confi-  \n\n| Research Article | Journal of Optical Communications and Networking 2 |\n| --- | --- |\n\nFig. 1. Overview of the novel detection strategy developed to detect soft fai","cbCailmbSTHLjAw8","https://ap.wps.com/l/cbCailmbSTHLjAw8","pdf",4035996,1,10,"English","en",105,"# 1. INTRODUCTION\n## Automated fault management and zero-touch principles\n## Telemetry data security and confidentiality preservation\n## Prior privacy approaches: encryption and third-party analytics\n## Proposed homomorphic solution and evaluation setup","[{\"question\":\"What problem does the paper address in optical communication networks?\",\"answer\":\"The paper addresses confidentiality and safety concerns when large telemetry datasets are processed for automated fault management, especially in third-party cloud and disaggregated scenarios.\"},{\"question\":\"How does the proposed method protect telemetry confidentiality?\",\"answer\":\"It uses feature scrambling to conceal the full content of collected telemetry data, allowing third-party computational services to operate without direct access to the original data.\"},{\"question\":\"Which unsupervised algorithms are evaluated for soft-failure detection?\",\"answer\":\"The paper evaluates four unsupervised methods: factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition.\"}]","Confidentiality-preserving Machine Learning Algorithms for Soft-failure Detection in Optical Communication Networks | PDF",1785818025,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"confidentiality-preserving-machine-learning-algorithms-for-soft-failure-detection-in-optical-communication-networks","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/confidentiality-preserving-machine-learning-algorithms-for-soft-failure-detection-in-optical-communication-networks/123689/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in optical communication networks?","Question",{"text":75,"@type":76},"The paper addresses confidentiality and safety concerns when large telemetry datasets are processed for automated fault management, especially in third-party cloud and disaggregated scenarios.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method protect telemetry confidentiality?",{"text":80,"@type":76},"It uses feature scrambling to conceal the full content of collected telemetry data, allowing third-party computational services to operate without direct access to the original data.",{"name":82,"@type":73,"acceptedAnswer":83},"Which unsupervised algorithms are evaluated for soft-failure detection?",{"text":84,"@type":76},"The paper evaluates four unsupervised methods: factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]