Hu-fu-Reference-Cited by-同舟云学术

Hu-fu

Published:2022-08 Issue:12 Volume:15 Page:3582-3585
ISSN:2150-8097
Container-title:Proceedings of the VLDB Endowment
language:en
Short-container-title:Proc. VLDB Endow.

Author:

Pan Xuchen¹,Tong Yongxin¹,Xue Chunbo¹,Zhou Zimu²,Du Junping³,Zeng Yuxiang⁴,Shi Yexuan¹,Zhang Xiaofei⁵,Chen Lei⁴,Xu Yi¹,Xu Ke¹,Lv Weifeng¹

Affiliation:

1. Beihang University, China

2. Singapore Management University

3. Beijing University of Posts and Telecommunications

4. The Hong Kong University of Science and Technology

5. University of Memphis

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Link

https://dl.acm.org/doi/pdf/10.14778/3554821.3554849

Reference10 articles.

1. SMCQL

2. Apache Calcite

3. 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.

4. Privacy preserving decision tree learning over multiple parties

5. 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.

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Responsible Sharing of Spatiotemporal Data;Companion of the 2024 International Conference on Management of Data;2024-06-09

2. FedCTQ: A Federated-Based Framework for Accurate and Efficient Contact Tracing Query;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

3. Spark-Based Virtual Data Warehouse Design;Lecture Notes in Electrical Engineering;2024

4. TEE-based General-purpose Computational Backend for Secure Delegated Data Processing;Proceedings of the ACM on Management of Data;2023-12-08

5. A survey on federated learning: a perspective from multi-party computation;Frontiers of Computer Science;2023-12-02