[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-167588-en":3,"doc-seo-167588-105":30,"detail-sidebar-cat-1-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":11,"category_id":12,"category_name":13,"doc_title":14,"doc_description":15,"doc_content":16,"file_id":17,"file_url":18,"file_type":19,"file_size":20,"view_count":4,"is_deleted":4,"is_public":11,"is_downloadable":11,"audit_status":11,"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":15,"update_tm":28,"read_time":29},167588,8796095027276,"wps_ap_test_251126_0180","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",1,11,"Presentations","Media Summary - Container-Based Validation for Confidential Data","Vilhuber discusses using containers as a modern mechanism to validate research on confidential data at scale. Containers simulate complete computer and software environments, improving reproducibility and portable reliability compared with traditional synthetic-data validation approaches. The method enables outsourcing infrastructure to commercial providers or users with low cost for data custodians, while still supporting validation against actual confidential data to reduce time and monetary expenses. Key remaining challenge is the lack of automated output-vetting algorithms at statistical agencies.","Using Containers to Validate Research on Confidential Data at Scale\nLars Vilhuber\nI describe past experience with the validation server process over 10 years and several hundred users, as a means to provide proxy access to confidential data. As a modern replacement, I propose the use of containers: simulated computers that encapsulate entire software and file structures. The use of containers ensures reproducibility and reliable portability and enables scalability. Infrastructure can be outsourced to commercial providers or users, at little to no cost to data providers. The only likely limitation to full automation is the absence of automated output-vetting algorithms at statistical agencies.\nKeywords: synthetic data, verification server, confidential data, reproducibility, containers\nMedia Summary\nVilhuber describes how researchers have used synthetic data as a proxy access to confidential data in the past. Such systems have been time-consuming and expensive to develop, because of trying to satisfy many different constraints on their suitability imposed by users. Instead, he suggests that modern containers—simulations of entire computer and software systems—could be leveraged as a portable, scalable, and cheaper replacement system. By packaging lower quality synthetic data in prescribed environments, and allowing users and commercial providers to support their use, then providing rapid turnaround on validation against the actual confidential data, monetary and time costs of the system for both providers and end users can be substantially reduced.\n1. Introduction\nConcerns about confidentiality in statistical products have increased in the past several years. While the new disclosure avoidance techniques introduced for the 2020 United States Decennial Census (\u0013 HYPERLINK \\l \"ref-ny57mrxu5eb\" \\h \u0014n.d.-a\u0015) garnered much attention, the academic community also expressed concerns about agency plans to apply formal disclosure avoidance techniques to public use microdata files (PUMFs), and after an initial announcement, the Census Bureau delayed implementation of such methods for the American Community Survey (ACS) (\u0013 HYPERLINK \\l \"ref-ngyjckmtm4y\" \\h \u0014n.d.-b\u0015).\nPart of the concern is that the application of confidentiality methods may prevent inferences that were feasibly made in the absence of those confidentiality procedures. Some of those concerns may be solved by adopting analysis methods that are congenial (\u0013 HYPERLINK \\l \"ref-nct4etwcul0\" \\h \u0014n.d.-c\u0015) to the disclosure avoidance methods, though that is not a recent problem (\u0013 HYPERLINK \\l \"ref-ndvdx622c07\" \\h \u0014n.d.-d\u0015). However, statistical agencies often attempt to provide ‘one-size-fits-all’ data sets for general purpose usage, as in the case of the ACS, with the goal of usability by a wide spectrum of models. One novel approach, at least at the time, was the provision of synthetic data that preserved most of the inferential validity of the underlying confidential data (\u0013 HYPERLINK \\l \"ref-n220xhzo138\" \\h \u0014n.d.-e\u0015); (\u0013 HYPERLINK \\l \"ref-nes8b2n0cok\" \\h \u0014n.d.-f\u0015); (\u0013 HYPERLINK \\l \"ref-noudiyh4mqr\" \\h \u0014n.d.-g\u0015); (\u0013 HYPERLINK \\l \"ref-nzv8769dwai\" \\h \u0014n.d.-h\u0015). Producing such general purpose synthetic data that are reasonably nondisclosive has proven difficult and long. As an alternative, data custodians provide confidential data to researchers in restricted-access environments, often still requiring physical presence in expensive-to-maintain secure computer rooms managed by the data custodian or universities (\u0013 HYPERLINK \\l \"ref-njtj4xq4wz9\" \\h \u0014n.d.-i\u0015); (\u0013 HYPERLINK \\l \"ref-nu349crrhtl\" \\h \u0014n.d.-j\u0015).\nThis article sets out to combine various lessons learned from both the literature and past experiments in a variety of domains to propose an alternative or complementary approach to providing access to confidential data via synthetic data or physical access, reducing the effort required by researchers to access these data. I draw on lessons learned from the analysis of several tho","cbCaikEHppBRWi9Q","https://ap.wps.com/l/cbCaikEHppBRWi9Q","docx",956758,42,"English","en",105,"# Introduction\n## Confidentiality concerns in statistical products\n## Synthetic data as proxy access\n## Restricted-access validation environments\n## Container-based validation approach\n# Validation and verification server concepts","[{\"question\":\"Why does the document propose containers for validating confidential data?\",\"answer\":\"It argues that containers encapsulate software and file structures to ensure reproducibility, reliable portability, and scalable validation against actual confidential data.\"},{\"question\":\"How does this approach compare with traditional synthetic data systems?\",\"answer\":\"Traditional systems were time-consuming and expensive because they had to satisfy many suitability constraints for users; container-based environments aim to make synthetic-data validation more portable, scalable, and cheaper.\"},{\"question\":\"What is the main limitation noted for fully automated use?\",\"answer\":\"The document highlights the likely absence of automated output-vetting algorithms at statistical agencies as the main barrier to full automation.\"}]","Media Summary - Container-Based Validation for Confidential Data | DOCX",1788216644,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":14,"keywords":34,"description":15,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"media-summary-container-based-validation-for-confidential-data","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":11},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/template/","Template",2,{"item":49,"name":13,"@type":43,"position":50},"https://docshare.wps.com/template/presentations/",3,{"item":52,"name":14,"@type":43,"position":53},"https://docshare.wps.com/template/media-summary-container-based-validation-for-confidential-data/167588/",4,{"url":52,"name":14,"@type":55,"author":56,"headline":14,"publisher":58,"fileFormat":61,"inLanguage":23,"description":15,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/vnd.openxmlformats-officedocument.wordprocessingml.document","2026-09-04","2026-08-31",true,{"@type":66,"interactionType":67,"userInteractionCount":47},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does the document propose containers for validating confidential data?","Question",{"text":76,"@type":77},"It argues that containers encapsulate software and file structures to ensure reproducibility, reliable portability, and scalable validation against actual confidential data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does this approach compare with traditional synthetic data systems?",{"text":81,"@type":77},"Traditional systems were time-consuming and expensive because they had to satisfy many suitability constraints for users; container-based environments aim to make synthetic-data validation more portable, scalable, and cheaper.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the main limitation noted for fully automated use?",{"text":85,"@type":77},"The document highlights the likely absence of automated output-vetting algorithms at statistical agencies as the main barrier to full automation.","https://schema.org",{"og:url":52,"og:type":88,"og:title":14,"og:site_name":59,"og:description":15},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,97,102,107,111,116,121,126,131],{"id":12,"doc_module":11,"doc_module_name":46,"category_name":13,"show_sort_weight":95,"slug":96},90,"presentations",{"id":98,"doc_module":11,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},12,"Resumes",80,"resumes",{"id":103,"doc_module":11,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},14,"Invoices",70,"invoices",{"id":29,"doc_module":11,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},"Posters",60,"posters",{"id":112,"doc_module":11,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},16,"Social Media",50,"social-media",{"id":117,"doc_module":11,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},17,"Forms",40,"forms",{"id":122,"doc_module":11,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},18,"Letters",30,"letters",{"id":127,"doc_module":11,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},21,"Paper Templates",5,"papers-templates",{"id":132,"doc_module":11,"doc_module_name":46,"category_name":133,"show_sort_weight":4,"slug":134},158,"General","general-158"]