[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125075-en":3,"doc-seo-125075-105":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},125075,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Functional Oblivious Transfer with Applications in Privacy-Preserving Machine Learning","Oblivious transfer (OT) enables a receiver to learn t out of n private messages without revealing which ones were chosen. This work introduces functional oblivious transfer (FOT), strengthening OT by letting the receiver learn only a function of the selected messages rather than the messages themselves. The paper proposes multiple protocols, including efficient unconditionally secure constructions for mean, mode, addition, and multiplication, plus an arbitrary-function protocol using FHE with constant O(1) FHE invocations in n. It further shows FOT improves privacy-preserving machine learning, including K-NN and Federated Learning client selection.","Functional Oblivious Transfer with Applications in Privacy-Preserving Machine Learning  \nAydin Abadi⋆1, Mohammad Naseri⋆⋆2  \n1 Newcastle University  \n2 Flower Labs  \nAbstract. Oblivious Transfer (OT) is a fundamental cryptographic primitive introduced nearly four decades ago. OT allows a receiver to select and learn t out of n private messages held by a sender.  \nIt ensures that the sender does not learn which specific messages the receiver has chosen, while the receiver gains no information about the remaining n − t messages. In this work, we introduce the notion of functional OT (FOT), for the first time. FOT adds a layer of security to the conventional OT by ensuring that the receiver only learns a function of the selected messages, rather than the t individual messages themselves. We propose several protocols that realize this concept. In particular, we propose concrete instantiations of FOT when the function to be executed on the selected message is mean, mode, addition, or multiplication. The schemes are efficient and unconditionally secure. We also propose a non-trivial protocol that supports arbitrary functions on the selected messages mainly using fully homomorphic encryption (FHE) and oblivious linear function evaluation, where the number of FHE invocations is constant O(1) with respect to n. Our asymptotic and concrete cost analyses demonstrate the efficiency of our unconditionally secure FOT protocols. FOT can enhance the security of privacy-preserving machine learning, particularly in (i) K-Nearest Neighbors schemes and (ii) client selection in Federated Learning (FL) .  \n1 Introduction  \nOblivious Transfer (OT) [19, 44, 57] is a vital cryptographic primitive that allows a receiver to select and learn t out of n messages held by a sender, where t ≥ 1 and n > t. In this setting, the sender must remain oblivious to which specific messages the receiver has chosen, while the receiver must gain no information about the remaining n − t messages. OT has applications in various domains, including secure multi-party computation [7,27,60], FL [46,58,59], private banking [17], and zero-knowledge proof systems [26] .  \nIn this work, we introduce the notion of functional oblivious transfer (FOT) . Conceptually, FOT enhances the security of conventional OT by enabling the receiver to learn only a certain function of the messages they select, rather than learning each selected message, while the sender remains as oblivious as in traditional OT, as shown in Figure 1 . We formally define FOT and present several instantiations of it. Specifically, we first introduce a functional OT protocol, ΓFOT2 , which relies on fully homomorphic encryption (FHE) and oblivious linear function evaluation (OLE) . This protocol supports arbitrary functions on the selected messages. While, in theory, secure computation can be achieved entirely using FHE or functional encryption [12], the primary challenge lies in designing protocols that minimize reliance on these primitives due to their high computational overhead. Addressing this challenge, ΓFOT2 ensures that the number of FHE invocations remains constant with respect to the total number of messages, n (and it is linear with t) . It is well-suited for scenarios where n − t is very large.  \nMoreover, we present efficient and scalable functional OT protocols (e.g. , ΓFOT3–Mean and ΓFOT3–Mode) which do not use any public key-based primitives. They are unconditionally secure and, as a result, are inherently post-quantum secure, provided the parties communicate over an unconditionally secure channel. These protocols mainly use a new combination of several techniques, including permutation maps, one-time pads, and padding. They also rely on a third party assumed to be susceptible to corruption by a semi-honest  \n⋆ [aydin.abadi@ncl.ac.uk](aydin.abadi@ncl.ac.uk)  \n⋆⋆ [mohammad@flower.ai](mohammad@flower.ai)  \nadversary. These protocols securely support various fundamental functions, namely, mode, mean, ad","cbCaigCnZHFh4yuh","https://ap.wps.com/l/cbCaigCnZHFh4yuh","pdf",1652578,1,35,"English","en",105,"# Introduction\n## Oblivious Transfer and Functional Oblivious Transfer\n## Summary of Contributions\n## Structure of the Paper","[{\"question\":\"What problem does functional oblivious transfer (FOT) solve compared with conventional OT?\",\"answer\":\"Conventional OT reveals neither which t messages were chosen to the sender nor information about the remaining messages to the receiver. FOT additionally ensures the receiver learns only a function of the selected messages instead of learning each selected message itself.\"},{\"question\":\"How does the paper construct arbitrary-function FOT protocols?\",\"answer\":\"It proposes a non-trivial protocol mainly using fully homomorphic encryption (FHE) and oblivious linear function evaluation (OLE), with the number of FHE invocations constant (O(1)) with respect to n.\"},{\"question\":\"Where can FOT improve privacy-preserving machine learning?\",\"answer\":\"The paper highlights two main scenarios: privacy-preserving K-nearest neighbors (K-NN) to reveal only predictions, and Federated Learning client selection so a server can choose clients without learning dataset or device details while keeping selection criteria hidden.\"}]","Functional Oblivious Transfer with Applications in Privacy-Preserving Machine Learning | PDF",1785896487,88,{"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},"functional-oblious-transfer-with-applications-in-privacy-preserving-machine-learning","",{"@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/functional-oblious-transfer-with-applications-in-privacy-preserving-machine-learning/125075/",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","2026-08-05",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 functional oblivious transfer (FOT) solve compared with conventional OT?","Question",{"text":75,"@type":76},"Conventional OT reveals neither which t messages were chosen to the sender nor information about the remaining messages to the receiver. FOT additionally ensures the receiver learns only a function of the selected messages instead of learning each selected message itself.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper construct arbitrary-function FOT protocols?",{"text":80,"@type":76},"It proposes a non-trivial protocol mainly using fully homomorphic encryption (FHE) and oblivious linear function evaluation (OLE), with the number of FHE invocations constant (O(1)) with respect to n.",{"name":82,"@type":73,"acceptedAnswer":83},"Where can FOT improve privacy-preserving machine learning?",{"text":84,"@type":76},"The paper highlights two main scenarios: privacy-preserving K-nearest neighbors (K-NN) to reveal only predictions, and Federated Learning client selection so a server can choose clients without learning dataset or device details while keeping selection criteria hidden.","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"]