[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117222-en":3,"doc-seo-117222-105":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},117222,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Privacy-Preserving Machine Learning over Distributed Data","PhD thesis investigating privacy-preserving machine learning when data are distributed across parties. The work studies secure training of logistic regression using secret sharing, detailing system model components and cryptographic primitives such as addition, multiplication, and matrix inversion. It also develops federated synthetic data generation with stronger security guarantees by combining generative adversarial networks with federated learning, homomorphic encryption, and differential privacy. Finally, it proposes PPDL for dropout-resilient aggregation in decentralized learning and evaluates efficiency, accuracy, and communication cost.","University of Groningen  \nPrivacy-Preserving Machine Learning over Distributed Data  \nGhavamipour, Ali Reza  \nDOI:  \n10.33612/diss.1139532528  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nGhavamipour, A. R. (2024) . Privacy-Preserving Machine Learning over Distributed Data. [Thesis fully internal (DIV), University of Groningen] . University of Groningen. [https://doi.org/10.33612/diss.1139532528](https://doi.org/10.33612/diss.1139532528)  \nCopyright  \nOther than for strictly personal use, it 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), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 12-12-2024  \nPrivacy-Preserving Machine Learning over Distributed Data  \nAli Reza Ghavamipour  \nThis research has been conducted at the Information Systems group of the Bernoulli Institute for Mathematics, Computer Science of the University of Groningen.  \nPrivacy-Preserving Machine Learning over Distributed Data  \nPhD thesis  \nto obtain the degree of PhD at the  \nUniversity of Groningen  \non the authority of the Rector Magnificus Prof. J.M.A. Scherpen and in accordance with  \nthe decision by the College of Deans.  \nThis thesis will be defended in public on Tuesday 3 December 2024 at 14.30 hours  \nby  \nAli Reza Ghavamipour  \nborn on 16 April 1989  \nin Tehran, Iran  \nSupervisors  \nProf. F. Turkmen  \nProf. D. Karastoyanova  \nAssessment Committee  \nProf. M. Onen  \nProf. S. Klous  \nProf. P. Van Ooijen  \nContents  \nAcknowledgements ix  \nList of figures xii  \nList of tables xiv  \nList of Acronyms xv  \n1 Introduction 1  \n1.1 Privacy-Preserving Machine Learning over Distributed Data ..... 2  \n1.2 Research questions and contributions .................. 4  \n1.3 Outline .................................... 6  \n2 Privacy-preserving Logistic Regression with Secret Sharing 9  \n2.1 Introduction ................................. 9  \n2.2 Related Work ................................ 11  \n2.3 Preliminaries ................................. 12  \n2.3.1 System model ............................ 12  \n2.3.2 Secret sharing ............................ 13  \n2.3.3 Addition and multiplication .................... 14  \n2.3.4 Matrix Inversion .......................... 15  \n2.3.5 Logistic Regression ......................... 16  \n2.4 Methods ................................... 16  \n2.4.1 Estimating model coefficients ................... 17  \n2.4.2 Gradient ............................... 17  \n2.4.3 Hessian ............................... 18  \n2.4.4 Privacy-preserving Logistic Regression Training ........ 19  \n2.5 Results .................................... 19  \n2.5.1 Implementation ","cbCailhSewcVlgFB","https://ap.wps.com/l/cbCailhSewcVlgFB","pdf",5516065,1,176,"English","en",105,"# Introduction\n## Privacy-Preserving Machine Learning over Distributed Data\n## Research questions and contributions\n## Outline\n# Privacy-preserving Logistic Regression with Secret Sharing\n## Introduction\n## Related Work\n## Preliminaries\n## Methods\n## Results\n# Federated Synthetic Data Generation with Stronger Security Guarantees\n## Introduction\n## Preliminary\n## Related Works\n## Approach\n## Experiments\n## Discussion\n## Conclusion\n# PPDL: Efficient Dropout-Resilient Aggregation for Privacy-Preserving Decentralized Learning\n## Introduction\n## Related work\n## Preliminaries\n## Problem Definition\n## Approaches","[{\"question\":\"What problem does the thesis address in privacy-preserving machine learning?\",\"answer\":\"It addresses how to perform machine learning when data are distributed, while protecting privacy during training and data generation.\"},{\"question\":\"How does the thesis implement privacy-preserving logistic regression?\",\"answer\":\"It uses secret sharing with methods for estimating model coefficients, computing gradients and Hessians, and training under privacy-preserving protocols.\"},{\"question\":\"What is PPDL and what improvement does it target?\",\"answer\":\"PPDL proposes efficient dropout-resilient aggregation for privacy-preserving decentralized learning, focusing on robustness during decentralized training.\"}]","Privacy-Preserving Machine Learning over Distributed Data | 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