More Web Proxy on the site http://driver.im/

research-article

A Framework for Privacy Preserving Localized Graph Pattern Query Processing

Authors:

Sourav S BhowmickAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 2

Article No.: 129, Pages 1 - 27

https://doi.org/10.1145/3589274

Published: 20 June 2023 Publication History

Abstract

This paper studies privacy preserving graph pattern query services in a cloud computing paradigm. In such a paradigm, data owner stores the large data graph to a powerful cloud hosted by a service provider (SP) and users send their queries to SP for query processing. However, as SP may not always be trusted, the sensitive information of users' queries, importantly, the query structures, should be protected. In this paper, we study how to outsource the localized graph pattern queries (LGPQs) on the SP side with privacy preservation. LGPQs include a rich set of semantics, such as subgraph homomorphism, subgraph isomorphism, and strong simulation, for which each matched graph pattern is located in a subgraph called ball that have a restriction on its size. To provide privacy preserving query service for LGPQs, this paper proposes the first framework, called Prilo, that enables users to privately obtain the query results. To further optimize Prilo, we propose Prilo* that comprises the first bloom filter for trees in the trust execution environment (TEE) on SP, a query-oblivious twiglet-based technique for pruning non-answers, and a secure retrieval scheme of balls that enables user to obtain query results early. We conduct detailed experiments on real world datasets to show that Prilo* is on average 4x faster than the baseline, and meanwhile, preserves query privacy.

Supplemental Material

MP4 File

Presentation video

Download
71.24 MB

References

[1]

Renzo Angles, János Benjamin Antal, Alex Averbuch, Altan Birler, Peter Boncz, Márton Búr, Orri Erling, Andrey Gubichev, Vlad Haprian, Moritz Kaufmann, et al. 2020. The LDBC social network benchmark. arXiv (2020).

[2]

Sergei Arnautov, Bohdan Trach, Franz Gregor, Thomas Knauth, Andre Martin, Christian Priebe, Joshua Lind, Divya Muthukumaran, Dan O'keeffe, Mark Stillwell, et al. 2016. SCONE: Secure linux containers with intel SGX. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Vol. 16. 689--703.

[3]

Maurice Bailleu, Jörg Thalheim, Pramod Bhatotia, Christof Fetzer, Michio Honda, and Kapil Vaswani. 2019. SPEICHER: Securing LSM-based key-value stores using shielded execution. In Proceedings of the 17th USENIX Conference on File and Storage Technologies (FAST). 173--190.

[4]

Johes Bater, Yongjoo Park, Xi He, Xiao Wang, and Jennie Rogers. 2020. Saqe: practical privacy-preserving approximate query processing for data federations. Proceedings of the VLDB Endowment (PVLDB), Vol. 13, 12 (2020), 2691--2705.

Digital Library

[5]

Christian Behrends, Mathew E Sowa, Steven P Gygi, and J Wade Harper. 2010. Network organization of the human autophagy system. Nature, Vol. 466, 7302 (2010), 68--76.

[6]

Dan Bogdanov, Sven Laur, and Jan Willemson. 2008. Sharemind: A framework for fast privacy-preserving computations. In 13th European Symposium on Research in Computer Security (ESORICS). Springer, 192--206.

Digital Library

[7]

Angela Bonifati, Wim Martens, and Thomas Timm. 2020. An analytical study of large SPARQL query logs. The VLDB Journal (VLDBJ), Vol. 29, 2--3 (2020), 655--679.

[8]

Jianneng Cao, Fang-Yu Rao, Mehmet Kuzu, Elisa Bertino, and Murat Kantarcioglu. 2013. Efficient tree pattern queries on encrypted xml documents. In Proceedings of the Joint EDBT/ICDT 2013 workshops (EDBT/ICDT). 111--120.

Digital Library

[9]

Ning Cao, Zhenyu Yang, Cong Wang, Kui Ren, and Wenjing Lou. 2011. Privacy-preserving query over encrypted graph-structured data in cloud computing. In IEEE 31st International Conference on Distributed Computing Systems (ICDCS). 393--402.

Digital Library

[10]

Zhao Chang, Lei Zou, and Feifei Li. 2016. Privacy preserving subgraph matching on large graphs in cloud. In Proceedings of the 2016 International Conference on Management of Data (SIGMOD). 199--213.

Digital Library

[11]

Stephen A Cook. 1971. The complexity of theorem-proving procedures. In Proceedings of the third annual ACM Symposium on Theory of Computing (STOC). 151--158.

Digital Library

[12]

Victor Costan and Srinivas Devadas. 2016. Intel SGX explained. Cryptology ePrint Archive (2016).

[13]

Ningning Cui, Xiaochun Yang, Bin Wang, Jianxin Li, and Guoren Wang. 2020. SVkNN: Efficient secure and verifiable k-nearest neighbor query on the cloud platform. In IEEE 36th International Conference on Data Engineering (ICDE). 253--264.

[14]

Grace Fan, Wenfei Fan, Yuanhao Li, Ping Lu, Chao Tian, and Jingren Zhou. 2020. Extending graph patterns with conditions. In Proceedings of the 2020 International Conference on Management of Data (SIGMOD). 715--729.

Digital Library

[15]

Zhe Fan, Byron Choi, Qian Chen, Jianliang Xu, Haibo Hu, and Sourav S. Bhowmick. 2015a. Structure-preserving subgraph query services. IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 27, 8 (2015), 2275--2290.

Digital Library

[16]

Zhe Fan, Byron Choi, Jianliang Xu, and Sourav S. Bhowmick. 2015b. Asymmetric structure-preserving subgraph queries for large graphs. In IEEE 31st International Conference on Data Engineering (ICDE). 339--350.

[17]

Jiuru Gao, Jiajie Xu, Guanfeng Liu, Wei Chen, Hongzhi Yin, and Lei Zhao. 2018. A privacy-preserving framework for subgraph pattern matching in cloud. In Database Systems for Advanced Applications: 23rd International Conference (DASFAA). 307--322.

Digital Library

[18]

Craig Gentry and Dan Boneh. 2009. A fully homomorphic encryption scheme.

[19]

Oded Goldreich and Rafail Ostrovsky. 1996. Software protection and simulation on oblivious RAMs. Journal of the ACM (JACM), Vol. 43, 3 (1996), 431--473.

Digital Library

[20]

Haibo Hu, Jianliang Xu, Qian Chen, and Ziwei Yang. 2012. Authenticating location-based services without compromising location privacy. In Proceedings of the 2012 International Conference on Management of Data (SIGMOD). 301--312.

Digital Library

[21]

Kai Huang, Haibo Hu, Shuigeng Zhou, Jihong Guan, Qingqing Ye, and Xiaofang Zhou. 2021. Privacy and efficiency guaranteed social subgraph matching. The VLDB Journal (VLDBJ) (2021), 1--22.

[22]

Qin Jiang, Yong Qi, Saiyu Qi, Wenjia Zhao, and Youshui Lu. 2020. Pbsx: A practical private boolean search using Intel SGX. Information Sciences, Vol. 521 (2020), 174--194.

Digital Library

[23]

Hyunjoon Kim, Yunyoung Choi, Kunsoo Park, Xuemin Lin, Seok-Hee Hong, and Wook-Shin Han. 2021. Versatile equivalences: Speeding up subgraph query processing and subgraph matching. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD). 925--937.

Digital Library

[24]

Hyunjoon Kim, Yunyoung Choi, Kunsoo Park, Xuemin Lin, Seok-Hee Hong, and Wook-Shin Han. 2022. Fast subgraph query processing and subgraph matching via static and dynamic equivalences. The VLDB Journal (VLDBJ) (2022), 1--26.

[25]

Hyeong-Il Kim, Hyeong-Jin Kim, and Jae-Woo Chang. 2019. A secure kNN query processing algorithm using homomorphic encryption on outsourced database. Data & Knowledge Engineering (DKE), Vol. 123 (2019), 101602.

Digital Library

[26]

Jinha Kim, Hyungyu Shin, Wook-Shin Han, Sungpack Hong, and Hassan Chafi. 2015. Taming subgraph isomorphism for RDF query processing. Proceedings of the VLDB Endowment (PVLDB), Vol. 8, 11 (2015), 1238--1249.

Digital Library

[27]

Daniel J Klionsky. 2008. Autophagy revisited: a conversation with Christian de Duve. Autophagy, Vol. 4, 6 (2008), 740--743.

[28]

Shangqi Lai, Xingliang Yuan, Shi-Feng Sun, Joseph K Liu, Yuhong Liu, and Dongxi Liu. 2019. GraphSE$^2$: An encrypted graph database for privacy-preserving social search. In Proceedings of the 2019 Asia Conference on Computer and Communications Security (AsiaCCS). 41--54.

Digital Library

[29]

Jinsoo Lee, Wook-Shin Han, Romans Kasperovics, and Jeong-Hoon Lee. 2012. An in-depth comparison of subgraph isomorphism algorithms in graph databases. Proceedings of the VLDB Endowment (PVLDB), Vol. 6, 2 (2012), 133--144.

Digital Library

[30]

Xinyu Lei, Alex X Liu, Rui Li, and Guan-Hua Tu. 2019. SecEQP: A secure and efficient scheme for SkNN query problem over encrypted geodata on cloud. In IEEE 35th International Conference on Data Engineering (ICDE). 662--673.

[31]

Jure Leskovec and Andrej Krevl. 2014. SNAP datasets: Stanford large network dataset collection. http://snap.stanford.edu/data.

[32]

Yin Li, Dhrubajyoti Ghosh, Peeyush Gupta, Sharad Mehrotra, Nisha Panwar, and Shantanu Sharma. 2021. Prism: private verifiable set computation over multi-owner outsourced databases. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD). 1116--1128.

Digital Library

[33]

Yehuda Lindell and Jonathan Katz. 2014. Introduction to modern cryptography.

[34]

An Liu, Kai Zhengy, Lu Liz, Guanfeng Liu, Lei Zhao, and Xiaofang Zhou. 2015. Efficient secure similarity computation on encrypted trajectory data. In IEEE 31st International Conference on Data Engineering (ICDE). 66--77.

[35]

Jinfei Liu, Juncheng Yang, Li Xiong, and Jian Pei. 2017. Secure skyline queries on cloud platform. In IEEE 33rd International Conference on Data Engineering (ICDE). 633--644.

[36]

Rongxing Lu, Xiaodong Lin, Zhiguo Shi, and Jun Shao. 2014. PLAM: A privacy-preserving framework for local-area mobile social networks. In 2014 IEEE International Conference on Computer Communications (INFOCOM). 763--771.

[37]

Shuai Ma, Yang Cao, Wenfei Fan, Jinpeng Huai, and Tianyu Wo. 2014. Strong simulation: Capturing topology in graph pattern matching. ACM Transactions on Database Systems (TODS), Vol. 39, 1 (2014), 1--46.

Digital Library

[38]

Robin Milner. 1989. Communication and concurrency.

[39]

Kartik Nayak, Xiao Shaun Wang, Stratis Ioannidis, Udi Weinsberg, Nina Taft, and Elaine Shi. 2015. GraphSC: Parallel secure computation made easy. In 2015 IEEE Symposium on Security and Privacy (S&P). 377--394.

Digital Library

[40]

Miles Ohlrich, Carl Ebeling, Eka Ginting, and Lisa Sather. 1993. Subgemini: Identifying subcircuits using a fast subgraph isomorphism algorithm. In Proceedings of the 30th International Design Automation Conference (DAC). 31--37.

Digital Library

[41]

Pascal Paillier. 1999. Public-key cryptosystems based on composite degree residuosity classes. In Advances in Cryptology-EUROCRYPT'99: International Conference on the Theory and Application of Cryptographic Techniques Prague (EUROCRYPT). 223--238.

[42]

Yinglong Song, Huey Eng Chua, Sourav S Bhowmick, Byron Choi, and Shuigeng Zhou. 2018. BOOMER: Blending visual formulation and processing of p-homomorphic queries on large networks. In Proceedings of the 2018 International Conference on Management of Data (SIGMOD). 927--942.

Digital Library

[43]

Einat Sprinzak, Shmuel Sattath, and Hanah Margalit. 2003. How reliable are experimental protein--protein interaction data? Journal of Molecular Biology (JMB), Vol. 327, 5 (2003), 919--923.

[44]

Yuanyuan Tian and Jignesh M. Patel. 2008. Tale: A tool for approximate large graph matching. In IEEE 24th International Conference on Data Engineering (ICDE). 963--972.

[45]

Julian R Ullmann. 1976. An algorithm for subgraph isomorphism. Journal of the ACM (JACM), Vol. 23, 1 (1976), 31--42.

Digital Library

[46]

Sheng Wang, Yiran Li, Huorong Li, Feifei Li, Chengjin Tian, Le Su, Yanshan Zhang, Yubing Ma, Lie Yan, Yuanyuan Sun, et al. 2022a. Operon: an encrypted database for ownership-preserving data management. Proceedings of the VLDB Endowment (PVLDB), Vol. 15, 12 (2022), 3332--3345.

Digital Library

[47]

Songlei Wang, Yifeng Zheng, Xiaohua Jia, and Xun Yi. 2022b. PeGraph: A system for privacy-preserving and efficient search over encrypted social graphs. IEEE Transactions on Information Forensics and Security (TIFS), Vol. 17 (2022), 3179--3194.

Digital Library

[48]

Songlei Wang, Yifeng Zheng, Xiaohua Jia, and Xun Yi. 2022c. Privacy-preserving analytics on decentralized social graphs: The case of eigendecomposition. IEEE Transactions on Knowledge and Data Engineering (TKDE) 1 (2022), 1--15.

[49]

Songrui Wu, Qi Li, Guoliang Li, Dong Yuan, Xingliang Yuan, and Cong Wang. 2019. Servedb: Secure, verifiable, and efficient range queries on outsourced database. In IEEE 35th International Conference on Data Engineering (ICDE). 626--637.

[50]

Cheng Xu, Ce Zhang, Jianliang Xu, and Jian Pei. 2021b. SlimChain: scaling blockchain transactions through off-chain storage and parallel processing. Proceedings of the VLDB Endowment (PVLDB), Vol. 14, 11 (2021), 2314--2326.

Digital Library

[51]

Lyu Xu, Byron Choi, Yun Peng, Jianliang Xu, and Sourav S Bhowmick. 2023. Technical report: A framework for privacy preserving localized graph pattern query processing. https://www.comp.hkbu.edu.hk/%7Ecslyuxu/prilo2023tr.pdf.

[52]

Lyu Xu, Jiaxin Jiang, Byron Choi, Jianliang Xu, and Sourav S Bhowmick. 2021a. Privacy preserving strong simulation queries on large graphs. In IEEE 37th International Conference on Data Engineering (ICDE). 1500--1511.

[53]

Xifeng Yan, Philip S Yu, and Jiawei Han. 2004. Graph indexing: a frequent structure-based approach. In Proceedings of the 2004 International Conference on Management of Data (SIGMOD). 335--346.

Digital Library

[54]

Can Zhang, Liehuang Zhu, Chang Xu, Kashif Sharif, Chuan Zhang, and Ximeng Liu. 2020. PGAS: Privacy-preserving graph encryption for accurate constrained shortest distance queries. Information Sciences, Vol. 506 (2020), 325--345.

Digital Library

[55]

Yandong Zheng, Rongxing Lu, Yunguo Guan, Songnian Zhang, Jun Shao, and Hui Zhu. 2022. Efficient and privacy-preserving similarity query with access control in eHealthcare. IEEE Transactions on Information Forensics and Security (TIFS), Vol. 17 (2022), 880--893.

[56]

Wenchao Zhou, Yifan Cai, Yanqing Peng, Sheng Wang, Ke Ma, and Feifei Li. 2021. Veridb: An sgx-based verifiable database. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD). 2182--2194.

Digital Library

[57]

Lei Zou, Lei Chen, and M Tamer Özsu. 2009. Distance-join: Pattern match query in a large graph database. Proceedings of the VLDB Endowment (PVLDB), Vol. 2, 1 (2009), 886--897.

Digital Library

Cited By

Yin BZhang PChen T(2024)Efficient State Sharding in Blockchain via Density-based Graph PartitioningACM Transactions on the Web10.1145/3697840Online publication date: 27-Sep-2024
https://dl.acm.org/doi/10.1145/3697840
Zhou YGuo QFang YMa C(2024)A Counting-based Approach for Efficient k-Clique Densest Subgraph DiscoveryProceedings of the ACM on Management of Data10.1145/36549222:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654922
Pang JMa CFang YChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Similarity-based Approach for Efficient Large Quasi-clique DetectionProceedings of the ACM Web Conference 202410.1145/3589334.3645374(401-409)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645374
Show More Cited By

Index Terms

A Framework for Privacy Preserving Localized Graph Pattern Query Processing
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
        Query optimization
2. Security and privacy

Recommendations

A graph query language and its query processing
Privacy-Preserving Top-k Query Processing in Distributed Systems
Transactions on Large-Scale Data- and Knowledge-Centered Systems XLII
Abstract
We consider a distributed system that stores user sensitive data across multiple nodes. In this context, we address the problem of privacy-preserving top-k query processing. We propose a novel system, called SD-TOPK, which is able to evaluate top-...
Query preserving graph compression
SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

It is common to find graphs with millions of nodes and billions of edges in, e.g., social networks. Queries on such graphs are often prohibitively expensive. These motivate us to propose query preserving graph compression, to compress graphs relative to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 2

PACMMOD

June 2023

2310 pages

EISSN:2836-6573

DOI:10.1145/3605748

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

Issue’s Table of Contents

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2023

Published in PACMMOD Volume 1, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Author Tags

Qualifiers

Research-article

Funding Sources

NSF of China for Joint Fund Project
HKRGC
NSFC

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
327
Total Downloads

Downloads (Last 12 months)155
Downloads (Last 6 weeks)5

Reflects downloads up to 23 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yin BZhang PChen T(2024)Efficient State Sharding in Blockchain via Density-based Graph PartitioningACM Transactions on the Web10.1145/3697840Online publication date: 27-Sep-2024
https://dl.acm.org/doi/10.1145/3697840
Zhou YGuo QFang YMa C(2024)A Counting-based Approach for Efficient k-Clique Densest Subgraph DiscoveryProceedings of the ACM on Management of Data10.1145/36549222:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654922
Pang JMa CFang YChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Similarity-based Approach for Efficient Large Quasi-clique DetectionProceedings of the ACM Web Conference 202410.1145/3589334.3645374(401-409)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645374
Peng YLin SChen QWang SXu LRen XLi YXu J(2024)ChatGraph: Chat with Your Graphs2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00424(5445-5448)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00424
Huang KLi YYe QTian YZhao XCui YHu HZhou X(2024)FRESH: Towards Efficient Graph Queries in an Outsourced Graph2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00346(4545-4557)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00346
Ma CFang YCheng RLakshmanan LHan XLi X(2023)Accelerating directed densest subgraph queries with software and hardware approachesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00805-033:1(207-230)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.1007/s00778-023-00805-0
Cheng XZhang FPeng YWang XHuang TZhou ZWong DDong C(2023)A Survey of Privacy Preserving Subgraph Matching MethodsArtificial Intelligence Security and Privacy10.1007/978-981-99-9785-5_8(98-113)Online publication date: 3-Dec-2023
https://dl.acm.org/doi/10.1007/978-981-99-9785-5_8

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents