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.

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Cited By

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 on Web Conference 202410.1145/3589334.3645374(401-409)Online publication date: 13-May-2024
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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
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

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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

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Copyright © 2023 ACM.

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Publication History

Published: 20 June 2023

Published in PACMMOD Volume 1, Issue 2

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Cited By

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 on 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

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