{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T05:14:36Z","timestamp":1778649276798,"version":"3.51.4"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>The increasing concerns on data security limit the sharing of data distributedly stored at multiple data owners and impede the scale of spatial queries over big urban data. In response, data federation systems have emerged to perform secure queries across multiple data owners leveraging secure multi-party computation. However, existing systems are designed for relational data. They are highly inefficient on spatial queries and limited in usability. In this demonstration, we introduce Hu-Fu, the first data federation system for secure spatial queries with high efficiency and usability. Hu-Fu is designed from the perspectives of the query user and the data owner for high usability and decomposes a spatial query into as many plaintext operators and as few secure operators as possible for high efficiency. We demonstrate the deployment and usage of Hu-Fu via cross-company taxi-calling, a popular smart city application.<\/jats:p>","DOI":"10.14778\/3554821.3554849","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3582-3585","source":"Crossref","is-referenced-by-count":12,"title":["Hu-fu"],"prefix":"10.14778","volume":"15","author":[{"given":"Xuchen","family":"Pan","sequence":"first","affiliation":[{"name":"Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongxin","family":"Tong","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunbo","family":"Xue","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zimu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Singapore Management University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junping","family":"Du","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yexuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Memphis"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Xu","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Xu","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weifeng","family":"Lv","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/3055330.3055334"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3190662"},{"key":"e_1_2_1_3_1","volume-title":"Sharemind: A Framework for Fast Privacy-Preserving Computations. In ESORICS. 192--206.","author":"Bogdanov Dan","year":"2008","unstructured":"Dan Bogdanov , Sven Laur , and Jan Willemson . 2008 . Sharemind: A Framework for Fast Privacy-Preserving Computations. In ESORICS. 192--206. Dan Bogdanov, Sven Laur, and Jan Willemson. 2008. Sharemind: A Framework for Fast Privacy-Preserving Computations. In ESORICS. 192--206."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2007.02.004"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Pawel Jurczyk and Li Xiong. 2011. Information Sharing across Private Databases: Secure Union Revisited. In SocialCom\/PASSAT. 996--1003.  Pawel Jurczyk and Li Xiong. 2011. Information Sharing across Private Databases: Secure Union Revisited. In SocialCom\/PASSAT. 996--1003.","DOI":"10.1109\/PASSAT\/SocialCom.2011.204"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/96602.96604"},{"key":"e_1_2_1_7_1","unstructured":"Numerous Beijing Taxi Brands to Collectively Connect to Amap's Ride-hailing Platform to Enable Online Operation. 2021. https:\/\/aag.cc\/newsinfo\/517126.html  Numerous Beijing Taxi Brands to Collectively Connect to Amap's Ride-hailing Platform to Enable Online Operation. 2021. https:\/\/aag.cc\/newsinfo\/517126.html"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514064"},{"key":"e_1_2_1_9_1","volume-title":"The EU General Data Protection Regulation (GDPR): A Practical Guide","author":"Voigt Paul","unstructured":"Paul Voigt and Axel Von dem Bussche . 2017. The EU General Data Protection Regulation (GDPR): A Practical Guide . Vol. 10 . Springer International Publishing . Paul Voigt and Axel Von dem Bussche. 2017. The EU General Data Protection Regulation (GDPR): A Practical Guide. Vol. 10. Springer International Publishing."},{"key":"e_1_2_1_10_1","first-page":"1","article-title":"Conclave: secure multi-party computation on big data","volume":"3","author":"Volgushev Nikolaj","year":"2019","unstructured":"Nikolaj Volgushev , Malte Schwarzkopf , Ben Getchell , Mayank Varia , Andrei Lapets , and Azer Bestavros . 2019 . Conclave: secure multi-party computation on big data . In EuroSys. 3 : 1 -- 3 :18. Nikolaj Volgushev, Malte Schwarzkopf, Ben Getchell, Mayank Varia, Andrei Lapets, and Azer Bestavros. 2019. Conclave: secure multi-party computation on big data. 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