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This paper investigates how k-anonymisation and dataset characteristics affect energy consumption during machine learning (ML) training, using three UCI datasets and comparing Random Forest (RF), k-Nearest neighbours (KNN), and Logistic Regression (LR) on both kanonymised and original data. Results show significant energy reductions for RF and LR, with larger savings for LR when more features are present, while KNN savings largely do not generalize.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploring-the-impact-of-k-anonymisation-on-the-energy-efficiency-of-machine-learning-algorithms-research-article/291216/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploring-the-impact-of-k-anonymisation-on-the-energy-efficiency-of-machine-learning-algorithms-research-article/291216.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What question does the paper address about AI and energy consumption?","Question",{"text":113,"@type":114},"It examines how k-anonymisation and dataset characteristics influence energy consumption during machine learning training.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning algorithms are evaluated in the study?",{"text":118,"@type":114},"Random Forest (RF), k-Nearest neighbours (KNN), and Logistic Regression (LR) are trained on both kanonymised and original datasets.",{"name":120,"@type":111,"acceptedAnswer":121},"What overall effect does k-anonymisation have on energy efficiency?",{"text":122,"@type":114},"The study finds k-anonymisation significantly reduces energy consumption for Random Forest and Logistic Regression, with energy savings for Logistic Regression increasing when more dataset features are present.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291216,1789643764,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","UvA-DARE (Digital Academic Repository)  \nExploring the Impact of K-Anonymisation on the Energy Efficiency of Machine Learning Algorithms  \nZemanek, V. ; Hu, Y. ; de Reus, P. ; Oprescu, A. ; Malavolta, I.  \nDOI  \n10.1109/ict4s64576.2024.00022  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nPublished in  \n2024 10th International Conference on ICT for Sustainability  \nLicense  \nArticle 25fa Dutch Copyright Act ( [https://www.openaccess.nl/en/policies/open-access-in](https://www.openaccess.nl/en/policies/open-access-in)dutch-copyright-law-taverne-amendment)  \nLink to publication  \nCitation for published version (APA):  \nZemanek, V. , Hu, Y. , de Reus, P. , Oprescu, A. , & Malavolta, I. (2024) . Exploring the Impact of K-Anonymisation on the Energy Efficiency of Machine Learning Algorithms. In 2024 10th International Conference on ICT for Sustainability: ICT4S 2024 : 24-28 June 2024, Stockholm, Sweden : proceedings (pp. 128-137) . IEEE Computer Society, Conference  \nPublishing Services. [https://doi.org/10.1109/ict4s64576.2024.00022](https://doi.org/10.1109/ict4s64576.2024.00022)  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:06 Jan 2026  \n2024 10th International Conference on ICT for Sustainability (ICT4S) ©2024 IEEE DOI: 10.1109/ICT4S64576.2024.00022| 979-8-3315-0528-8/24/$31.00 |   \n2024 10th International Conference on ICT for Sustainability (ICT4S)  \nExploring the Impact of K-Anonymisation on the Energy Ef􀀂ciency of Machine Learning Algorithms  \nVit Zemanek􀀃 Master Computer Science University of Amsterdam Amsterdam, Netherlands [vit.zemanek@student.uva.nl](vit.zemanek@student.uva.nl)  \nYixin Hu􀀃 Master Computer Science Vrije Universiteit Amsterdam, Netherlands [y.hu5@student.vu.nl](y.hu5@student.vu.nl)  \nPepijn de Reus  \nMaster Arti􀀂cial Intelligence University of Amsterdam Amsterdam, Netherlands [p.dereus@uva.nl](p.dereus@uva.nl)  \nAna Oprescu  \nComplex Cyber Infrastructure University of Amsterdam Amsterdam, Netherlands [a.m.oprescu@uva.nl](a.m.oprescu@uva.nl)  \nIvano Malavolta  \nS2 research group Vrije Universiteit Amsterdam, Netherlands [i.malavolta@vu.nl](i.malavolta@vu.nl)  \nAbstract—With the increased use of Arti􀀂cial Intelligence (AI), concerns about AI’s energy consumption are increasing as well. This paper investigates the impact of k-anonymisation and dataset characteristics on energy consumption during machine learning (ML) training. Using three datasets from the UCI Machine Learning Repository, we analyze the energy ef􀀂ciency of ML algorithms—Random Forest (RF), k-Nearest neighbours (KNN), and Logistic Regression (LR)—trained on both kanonymised and original datasets. Our experiment reveals that kanonymisation signi􀀂cantly reduces energy consumption during Random Forest (RF) and Logistic Regression (LR) training. Additionally, we 􀀂nd that k-anonymisation leads to greater energy savings in Logistic Regression (LR) training if more features are present in the dataset. However, we also 􀀂nd that the energy sa","cbCaiaATwIbqgHKn","https://ap.wps.com/l/cbCaiaATwIbqgHKn","pdf",408335,11,"English","# Abstract\n# Introduction\n# Index Terms\n# Main Content","[{\"question\":\"What question does the paper address about AI and energy consumption?\",\"answer\":\"It examines how k-anonymisation and dataset characteristics influence energy consumption during machine learning training.\"},{\"question\":\"Which machine learning algorithms are evaluated in the study?\",\"answer\":\"Random Forest (RF), k-Nearest neighbours (KNN), and Logistic Regression (LR) are trained on both kanonymised and original datasets.\"},{\"question\":\"What overall effect does k-anonymisation have on energy efficiency?\",\"answer\":\"The study finds k-anonymisation significantly reduces energy consumption for Random Forest and Logistic Regression, with energy savings for Logistic Regression increasing when more dataset features are present.\"}]","Exploring the Impact of K-Anonymisation on the Energy Efficiency of Machine Learning Algorithms - Research Article | PDF",28]