[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124326-en":3,"doc-seo-124326-105":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},124326,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","An Approach to Trade-off Privacy and Classification Accuracy in Machine Learning Processes - Research report","Machine learning over large distributed data can expose sensitive information, since even protected data may still enable re-identification through the high processing power of modern models. This discussion paper proposes a decision-support framework for data anonymization that uses relaxed functional dependencies to derive anonymization strategies and reason about privacy-utility trade-offs. The framework can combine multiple correlations to increase dataset utility, while experiments on real datasets show improved utility under the targeted privacy level.","An Approach to Trade-off Privacy and Classification Accuracy in Machine Learning Processes  \nCitation for published version (APA):  \nCaruccio, L. , Desiato, D. , Polese, G. , Tortora, G. , & Zannone, N. (2023) . An Approach to Trade-off Privacy and Classification Accuracy in Machine Learning Processes. In D. Calvanese, C. Diamantini, G. Faggioli, N. Ferro, S. Marchesin, G. Silvello, & L. Tanca (Eds.), SEBD 2023 : 31st Symposium of Advanced Database Systems:  \nProceedings of the 31st Symposium of Advanced Database Systems : Galzingano Terme, Italy, July 2nd to 5th, 2023 (pp. 420-429) . (CEUR Workshop Proceedings; Vol. 3478) . [CEUR-WS.org](CEUR-WS.org). [https://ceur-ws.org/Vol-](https://ceur-ws.org/Vol-)[ ](https://ceur-ws.org/Vol-)[3478/paper72.pdf](3478/paper72.pdf)  \nDocument license:  \nCC BY  \nDocument status and date:  \nPublished: 01/01/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 08. Sep. 2025  \nAn Approach to Trade-off Privacy and Classification Accuracy in Machine Learning Processes  \nLoredana Caruccio1 , Domenico Desiato2 , Giuseppe Polese1 , Genoveffa Tortora1 and Nicola Zannone3  \n1 Department of Computer Science, University of Salerno, via Giovanni Paolo II n. 132, 84084 Fisciano (SA), Italy 2 Department of Computer Science, University of Bari Aldo Moro, via Edoardo Orabona n.4, 70125 Bari (BA), Italy  \n3 Eindhoven University of Technology, Eindhoven, Netherlands  \nAbstract  \nMachine learning techniques applied to large and distributed data archives might result in the disclosure of sensitive information. Data often contain sensitive identifiable information, and even if these are protected, the excessive processing capabilities of current machine learning techniques might facilitate the identification of individuals. This discussion paper presents a decision-support framework for data anonymization. The latter relies on a novel approach that exploits data correlations, expressed in terms of relaxed functional dependencies (rfds), to identify data anonymization strategies for providing suitable trade-offs between privacy and data utility. It also permits to generate anonymization strategies leveraging multiple data correlations simultaneously t","cbCaifio4zeiPrCU","https://ap.wps.com/l/cbCaifio4zeiPrCU","pdf",765280,1,11,"English","en",105,"# Introduction\n## Problem: privacy risks in Big Data analytics\n# Proposed Framework\n## Decision-support for anonymization strategies\n## Using relaxed functional dependencies (rfds)\n## Exploiting multiple data correlations\n# Experimental Evaluation\n## Results on real-life datasets\n# Trade-off Analysis\n## Understanding privacy vs data utility offered by strategies\n# Author Support and Selection\n## Enabling data owners to choose strategies","[{\"question\":\"Why can machine learning processes still disclose sensitive information even when data are protected?\",\"answer\":\"Sensitive identifiable information can enable re-identification, and the excessive processing capabilities of current machine learning techniques may facilitate identification of individuals despite protection mechanisms.\"},{\"question\":\"What is the core idea of the proposed decision-support framework?\",\"answer\":\"It supports data anonymization by using a novel approach that exploits data correlations expressed as relaxed functional dependencies (rfds) to derive strategies with suitable privacy-utility trade-offs.\"},{\"question\":\"How does the framework improve the utility of anonymized datasets?\",\"answer\":\"It can generate anonymization strategies by leveraging multiple data correlations simultaneously, increasing the utility of the resulting anonymized datasets while maintaining the desired privacy level.\"}]","An Approach to Trade-off Privacy and Classification Accuracy in Machine Learning Processes - Research report | PDF",1785821620,28,{"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},"an-approach-to-trade-off-privacy-and-classification-accuracy-in-machine-learning-processes-research-report","",{"@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/an-approach-to-trade-off-privacy-and-classification-accuracy-in-machine-learning-processes-research-report/124326/",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},"Why can machine learning processes still disclose sensitive information even when data are protected?","Question",{"text":75,"@type":76},"Sensitive identifiable information can enable re-identification, and the excessive processing capabilities of current machine learning techniques may facilitate identification of individuals despite protection mechanisms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the core idea of the proposed decision-support framework?",{"text":80,"@type":76},"It supports data anonymization by using a novel approach that exploits data correlations expressed as relaxed functional dependencies (rfds) to derive strategies with suitable privacy-utility trade-offs.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the framework improve the utility of anonymized datasets?",{"text":84,"@type":76},"It can generate anonymization strategies by leveraging multiple data correlations simultaneously, increasing the utility of the resulting anonymized datasets while maintaining the desired privacy level.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]