[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84713-en":3,"doc-seo-84713-105":29,"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":20,"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":13,"seo_description":14,"update_tm":27,"read_time":28},84713,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Efficient and Secure Range Counting over Distributed Geographic Data with Query Range Protection","Range counting underpins geographic information systems, but distributed holdings across multiple organizations create severe privacy risks. Existing privacy-preserving protocols typically protect organization datasets while leaving efficiency, query confidentiality, and accuracy unresolved, especially when data overlap. PPRC is the first protocol to jointly satisfy these requirements by combining Private Range Predicate (PRP) for efficient encrypted point-in-range evaluation and Oblivious Linear Counting (OLC) for secure aggregation of overlapping partial results. Theoretical analysis and experiments show strong accuracy and speed improvements.","Efficient and Secure Range Counting over Distributed Geographic Data with Query Range Protection  \nHaoxin Yang 1 , Pinghui Wang 1, ∗ , Zhe Hou2 , Tian Zhou 1 , Guangmingzi Yang2 , Zehua Lei2 , Rundong Li 1 , Yutong Song 1 , Yongyuan Peng 1 , Fangming Dong 1 , Xiaohong Guan 1  \n1Xi’an Jiaotong University, 2 China Mobile System Integration Co., Ltd  \n{yhxaxx1,xjtulirundong,Sunny52012,butter0210field,dfming}@stu.xjtu.edu.cn,phwang@mail.xjtu.edu.cn  \n[hz4980@163.com](hz4980@163.com),{tianzhou,[xhguan}@xjtu.edu.cn](xhguan}@xjtu.edu.cn), [yangguangmingzi@126.com](yangguangmingzi@126.com), [leizehua@cmict.chinamobile.com](leizehua@cmict.chinamobile.com)  \narXiv :2607 .04 194v 1 [ cs .DS] 5 Jul 2026  \nABSTRACT  \nRange counting is a core primitive in geographic information systems. When data is distributed across multiple organizations, conducting range counting raises substantial privacy concerns. Existing privacy-preserving protocols focus on protecting organizations’datasets, but cannot simultaneously achieve efficiency, query privacy, and accuracy on overlapping data. Typical protocols process query range in plaintext for efficient point-in-range evaluation, since query-private designs rely on expensive secure comparisons. Moreover, most works assume non-overlapping datasets across organizations, which leads to huge errors in overlapping scenarios.  \nIn this paper, we propose PPRC, the first protocol that jointly satisfies all the privacy, efficiency, and accuracy requirements. PPRC makes two key technical contributions. First, we design the Private Range Predicate (PRP) technique that supports efficient point-inrange evaluation while protecting the query range. PRP reformulates range evaluation as encrypted membership tests, effectively replacing costly secure comparisons with faster secure multiplications. Second, we propose Oblivious Linear Counting (OLC), an aggregation scheme that efficiently and securely aggregates partial results from organizations with overlapping data. OLC involves only lightweight cryptographic operations and ensures that no information is leaked beyond the final range count. We theoretically analyze the accuracy, efficiency, and security of PPRC. Experiments on real-world and synthetic datasets show that PPRC achieves up to 55× smaller errors and 37× speedup compared to baseline protocols.  \nPVLDB Reference Format:  \nHaoxin Yang1 , Pinghui Wang1, ∗ , Zhe Hou2 , Tian Zhou1 , Guangmingzi Yang2 , Zehua Lei2 , Rundong Li1 , Yutong Song1 , Yongyuan Peng1 , Fangming Dong1 , Xiaohong Guan1 . Efficient and Secure Range Counting over Distributed Geographic Data with Query Range Protection. PVLDB, 19(2): XXX-XXX, 2020 .  \ndoi:XX.XX/XXX.XX  \nPVLDB Artifact Availability:  \nThe source code, data, and/or other artifacts have been made available at [https://github.com/jackson-maybe/PPRC](https://github.com/jackson-maybe/PPRC).  \nThis work is licensed under the Creative Commons BY-NC-ND 4.0 International License. Visit [https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of](https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of)[ ](https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of)[this license. For any use beyond those covered by this license](this license. For any use beyond those covered by this license), [obtain permission by](obtain permission by)[emailing info@vldb.org. Copyright](emailing info@vldb.org. Copyright) is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.  \nProceedings of the VLDB Endowment, Vol. 19, No. 2 ISSN 2150-8097 .  \ndoi:XX.XX/XXX.XX  \n1 INTRODUCTION  \nIn geographic information systems (GIS), range counting is a fundamental task that returns the number of distinct geographic records falling within a given query range. It supports a variety of geographic analysis and decision-making tasks.  \nWith the growing scale of data and the prevalence of multiorganizational data silos, range counting increasingly needs to be perf","cbCaiddSl3718v9d","https://ap.wps.com/l/cbCaiddSl3718v9d","pdf",1914879,1,13,"English","en",105,"# Introduction\n## Background and motivation\n## Privacy requirements and limitations of existing approaches","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses Distributed Range Counting (DRC), which computes the number of geographic records within a query range across multiple organizations’ distributed datasets.\"},{\"question\":\"Why is query range protection important?\",\"answer\":\"Query ranges can reveal sensitive information such as a user’s location or health status, so the protocol must prevent disclosure of the query range while still producing an aggregated count.\"},{\"question\":\"How does PPRC achieve both efficiency and privacy?\",\"answer\":\"PPRC uses Private Range Predicate (PRP) to replace expensive secure comparisons with faster secure multiplications for encrypted point-in-range evaluation, and uses Oblivious Linear Counting (OLC) to securely aggregate partial results even with overlapping data.\"}]",1784197797,33,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"efficient-and-secure-range-counting-over-distributed-geographic-data-with-query-range-protection","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/efficient-and-secure-range-counting-over-distributed-geographic-data-with-query-range-protection/84713/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper addresses Distributed Range Counting (DRC), which computes the number of geographic records within a query range across multiple organizations’ distributed datasets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is query range protection important?",{"text":80,"@type":76},"Query ranges can reveal sensitive information such as a user’s location or health status, so the protocol must prevent disclosure of the query range while still producing an aggregated count.",{"name":82,"@type":73,"acceptedAnswer":83},"How does PPRC achieve both efficiency and privacy?",{"text":84,"@type":76},"PPRC uses Private Range Predicate (PRP) to replace expensive secure comparisons with faster secure multiplications for encrypted point-in-range evaluation, and uses Oblivious Linear Counting (OLC) to securely aggregate partial results even with overlapping 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