[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-157015-105":3,"doc-detail-157015-en":81,"detail-sidebar-cat-0-en-105":98},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","zinnia-expressive-efficient-zero-knowledge-framework-for-general-purpose-data-analytics","Zinnia - Expressive, Efficient Zero-Knowledge Framework for General-Purpose Data Analytics","","Data analytics enables insight discovery, yet verification is hard when results rely on private or proprietary data and the audience cannot re-execute the computation. Zero-Knowledge Proofs (ZKPs) address this by proving correct execution without revealing underlying data, enabling verifiable computation. The paper presents Zinnia, an expressive and efficient ZKP framework for general-purpose analytics, using a domain-specific language and symbolic execution to compile workflows into optimized ZKP circuits, supporting rich control flow and real-valued arithmetic.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/zinnia-expressive-efficient-zero-knowledge-framework-for-general-purpose-data-analytics/157015/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/zinnia-expressive-efficient-zero-knowledge-framework-for-general-purpose-data-analytics/157015.png","ImageObject",300,407,{"name":42,"@type":43},"Genevieve","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-20","2026-08-29",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is verifying data-analytics claims difficult in practice?","Question",{"text":63,"@type":64},"Once analysts publish results, stakeholders must either trust the analyst or re-execute the computation. Re-execution is often infeasible when data is private or proprietary, limiting transparency and credibility.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What do Zero-Knowledge Proofs enable for data analytics?",{"text":68,"@type":64},"ZKPs allow a prover to convince a verifier that a computation was evaluated correctly on secret inputs without revealing those inputs beyond the claim itself.",{"name":70,"@type":61,"acceptedAnswer":71},"What is Zinnia’s main approach for supporting general-purpose analytics?",{"text":72,"@type":64},"Zinnia introduces a high-level DSL plus a symbolic execution engine that reasons about analytics programs and compiles them into optimized ZKP circuits, enabling richer language features such as data-dependent control flow and real-valued arithmetic.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},157015,1787971204,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":92,"language":93,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":94,"faqs":95,"seo_title":96,"seo_description":12,"update_tm":80,"read_time":97,"preview_page_count":4},1374391974585,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Zinnia: Expressive, Efficient Zero-Knowledge Framework for  \nGeneral-Purpose Data Analytics  \nZhantong Xue  \nHong Kong University of Science and Technology Hong Kong, China  \n[zxueai@cse.ust.hk](zxueai@cse.ust.hk)  \nZhaoyu Wang  \nHong Kong University of Science and Technology Hong Kong, China [zwangjz@cse.ust.hk](zwangjz@cse.ust.hk)  \nAbstract  \nData analytics is a powerful tool for uncovering patterns and generating insights. However, once a claim is madebased on data analysis, its audience must either trust the analyst or re-execute the analysis (often on private or proprietary data) to verify its correctness. This reliance raises significant concerns about transparency and trust in the analytics process. Zero-Knowledge Proofs (ZKPs), a cryptographic technique, offer a principled solution by enabling analysts to produce proofs of correctness without revealing the underlying data. This paradigm, known as verifiable computation, allows any verifier to check the validity of the analysis result solely from the proof.  \nIn this paper, we introduce Zinnia, a expressive and efficient ZKP framework designed for general-purpose data analytics. Zinnia provides a high-level domain-specific language (DSL) for encoding analytics workflows and a symbolic execution engine that reasons about the programs and compiles them into optimized ZKP circuits. Together, these components support rich language features such as data-dependent control flow (e.g., dynamic loops, recursion, early exits), real-valued arithmetic, non-linear functions and multidimensional array manipulations. We evaluate Zinnia’s usability through real-world case studies and a user study, and benchmark its performance across diverse analytics tasks. Zinnia achieves up to 5. 8× speedup over zkVMs and produces ZKP circuits that are 19. 3% smaller than those generated by existing zero-knowledge programming languages.  \n1 Introduction  \nData analytics underpins scientific discovery, operational decision making, and public-policy formulation. Modern analysts wield an extensive arsenal of ecosystems to ingest, transform, aggregate, model, and visualize data at every scale. The data analysts make claims about the results of their computations, but provide no means to verify the correctness of these claims. A straightforward solution is to release the raw data where the stakeholders can re-execute the computations and verify the results. However, this approach  \n∗ Corresponding author  \nPingchuan Ma∗ Hong Kong University of Science and Technology Hong Kong, China CipherInsight Limited Hong Kong, China [pmaab@cse.ust.hk](pmaab@cse.ust.hk)  \nShuai Wang∗ Hong Kong University of Science and Technology Hong Kong, China CipherInsight Limited Hong Kong, China [shuaiw@cse.ust.hk](shuaiw@cse.ust.hk)  \nAnalyst  \nStakeholder  \nFigure 1: Conceptual data-analytics scenario with ZKPs. Analysts can convince stakeholders of the validity of their results without revealing sensitive data.  \nis often infeasible when the data is private or proprietary. As a result, transparency and credibility of data analytics are increasingly challenged.  \nZKPs (Zero-Knowledge Proofs) [10] provide a principled solution to this problem. As shown in Fig. 1, ZKPs allow a prover to convince a verifier that a computational statement is evaluated correctly on secret inputs, while revealing nothing beyond the claim itself. As illustrated in Fig. 2, the mechanical workflow of ZKPs consists of four main steps: (1) the prover expresses their computational statement into a static arithmetic circuit, (2) the prover computes the output of the circuit together with an auxiliary witness,(3) the prover emits a succinct proof of correct execution, and (4) the verifier checks the proof against the public inputs and the arithmetic circuit. This paradigm enables verifiable computation where the prover can convince the verifier of the correctness of their computations without exposing private data. With the success of recent SNARK systems","cbCaiqrLc6wPzay4","https://ap.wps.com/l/cbCaiqrLc6wPzay4","pdf",1990824,20,"English","# Introduction\n## Data analytics and verification challenges\n## ZKPs and verifiable computation\n## Technical challenges for general-purpose analytics\n## Existing solutions and motivation","[{\"question\":\"Why is verifying data-analytics claims difficult in practice?\",\"answer\":\"Once analysts publish results, stakeholders must either trust the analyst or re-execute the computation. Re-execution is often infeasible when data is private or proprietary, limiting transparency and credibility.\"},{\"question\":\"What do Zero-Knowledge Proofs enable for data analytics?\",\"answer\":\"ZKPs allow a prover to convince a verifier that a computation was evaluated correctly on secret inputs without revealing those inputs beyond the claim itself.\"},{\"question\":\"What is Zinnia’s main approach for supporting general-purpose analytics?\",\"answer\":\"Zinnia introduces a high-level DSL plus a symbolic execution engine that reasons about analytics programs and compiles them into optimized ZKP circuits, enabling richer language features such as data-dependent control flow and real-valued arithmetic.\"}]","Zinnia - Expressive, Efficient Zero-Knowledge Framework for General-Purpose Data Analytics | PDF",50,{"code":4,"msg":82,"data":99},[100,104,108,112,117,121,126,129,133,136,140],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},"Exam",70,"exam",{"id":113,"doc_module":4,"doc_module_name":25,"category_name":114,"show_sort_weight":115,"slug":116},5,"Comic",60,"comic",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":119,"show_sort_weight":97,"slug":120},6,"Technology","technology",{"id":122,"doc_module":4,"doc_module_name":25,"category_name":123,"show_sort_weight":124,"slug":125},7,"Healthcare",40,"healthcare",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":127,"slug":128},30,"research-report",{"id":130,"doc_module":4,"doc_module_name":25,"category_name":131,"show_sort_weight":92,"slug":132},9,"Religion & Spirituality","religion-spirituality",{"id":92,"doc_module":4,"doc_module_name":25,"category_name":134,"show_sort_weight":92,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":25,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":25,"category_name":142,"show_sort_weight":113,"slug":143},19,"General","general"]