DSVD‐autoencoder: A scalable distributed privacy‐preserving method for one‐class classification

International Journal of Intelligent SystemsVolume 36, Issue 1 p. 177-199 RESEARCH ARTICLE DSVD-autoencoder: A scalable distributed privacy-preserving method for one-class classification Oscar Fontenla-Romero, Oscar Fontenla-Romero Department of Computer Science and Information Technologies, CITIC—Faculty of Informatics, Universidade da Coruña, A Coruña, SpainSearch for more papers by this authorBeatriz Pérez-Sánchez, Corresponding Author Beatriz Pérez-Sánchez beatriz.perezs@udc.es Department of Computer Science and Information Technologies, CITIC—Faculty of Informatics, Universidade da Coruña, A Coruña, Spain Correspondence Beatriz Pérez-Sánchez, Department of Computer Science and Information Technologies, CITIC—Faculty of Informatics, Universidade da Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain. Email: beatriz.perezs@udc.esSearch for more papers by this authorBertha Guijarro-Berdiñas, Bertha Guijarro-Berdiñas Department of Computer Science and Information Technologies, CITIC—Faculty of Informatics, Universidade da Coruña, A Coruña, SpainSearch for more papers by this author Oscar Fontenla-Romero, Oscar Fontenla-Romero Department of Computer Science and Information Technologies, CITIC—Faculty of Informatics, Universidade da Coruña, A Coruña, SpainSearch for more papers by this authorBeatriz Pérez-Sánchez, Corresponding Author Beatriz Pérez-Sánchez beatriz.perezs@udc.es Department of Computer Science and Information Technologies, CITIC—Faculty of Informatics, Universidade da Coruña, A Coruña, Spain Correspondence Beatriz Pérez-Sánchez, Department of Computer Science and Information Technologies, CITIC—Faculty of Informatics, Universidade da Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain. Email: beatriz.perezs@udc.esSearch for more papers by this authorBertha Guijarro-Berdiñas, Bertha Guijarro-Berdiñas Department of Computer Science and Information Technologies, CITIC—Faculty of Informatics, Universidade da Coruña, A Coruña, SpainSearch for more papers by this author First published: 10 October 2020 https://doi.org/10.1002/int.22296Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract One-class classification has gained interest as a solution to certain kinds of problems typical in a wide variety of real environments like anomaly or novelty detection. Autoencoder is the type of neural network that has been widely applied in these one-class problems. In the Big Data era, new challenges have arisen, mainly related with the data volume. Another main concern derives from Privacy issues when data is distributed and cannot be shared among locations. These two conditions make many of the classic and brilliant methods not applicable. In this paper, we present distributed singular value decomposition (DSVD-autoencoder), a method for autoencoders that allows learning in distributed scenarios without sharing raw data. Additionally, to guarantee privacy, it is noniterative and hyperparameter-free, two interesting characteristics when dealing with Big Data. In comparison with the state of the art, results demonstrate that DSVD-autoencoder provides a highly competitive solution to deal with very large data sets by reducing training from several hours to seconds while maintaining good accuracy. Citing Literature Volume36, Issue1January 2021Pages 177-199 RelatedInformation