SPG: Structure-Private Graph Database via SqueezePIR
Summary: SPG provides a structure-private graph database for GNN workloads by hiding access-pattern leakage (which node/neighbor is accessed) via PIR. Introduces SqueezePIR, a compression-optimized PIR yielding ~11.85× speedup vs FastPIR with <2% accuracy loss. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ling Liang (University of California Santa Barbara)
- 2. Jilan Lin (University of California Santa Barbara)
- 3. Zheng Qu (University of California Santa Barbara)
- 4. Ishtiyaque Ahmad (University of California Santa Barbara)
- 5. Fengbin Tu (University of California Santa Barbara)
- 6. Trinabh Gupta (University of California Santa Barbara)
- 7. Yufei Ding (University of California Santa Barbara)
- 8. Yuan Xie (Alibaba)
BibTeX Citation
@article{liang_vldb23,
title = {{SPG: Structure-Private Graph Database via SqueezePIR}},
author = {Liang, Ling and Lin, Jilan and Qu, Zheng and Ahmad, Ishtiyaque and Tu, Fengbin and Gupta, Trinabh and Ding, Yufei and Xie, Yuan},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {7},
pages = {1615--1628},
doi = {10.14778/3587136.3587138},
url = {https://doi.org/10.14778/3587136.3587138},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,817 | OpenFGL: A Comprehensive Benchmark for Federated Graph Learning | 2025 | VLDB | 5.093636e-05 |
| 10,921 | Sectric: Towards Accurate, Privacy-preserving and Efficient Triangle Counting | 2025 | VLDB | 5.093636e-05 |
| 11,469 | Private Information Retrieval in Large Scale Public Data Repositories | 2023 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 223 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00024182473 |
| 5,384 | Privacy Preserving Subgraph Matching on Large Graphs in Cloud | 2016 | SIGMOD | 6.2370509e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,021 | Graph Compression for Interpretable Graph Neural Network Inference At Scale | 2025 | VLDB |
| 2 | 8,477 | Cohesiveness-aware Hierarchical Compressed Index for Community Search on Attributed Graphs | 2025 | SIGMOD |
| 3 | 11,469 | Private Information Retrieval in Large Scale Public Data Repositories | 2023 | VLDB |
| 4 | 6,408 | CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression | 2023 | SIGMOD |
| 5 | 4,674 | Nearest Neighbor Search with Strong Location Privacy | 2010 | VLDB |
| 6 | 10,392 | PrivAGS: Differentially Private Attributed Graph Synthesis | 2026 | SIGMOD |
| 7 | 2,319 | Secure kNN Computation on Encrypted Databases | 2009 | SIGMOD |
| 8 | 10,913 | Inference-friendly Graph Compression for Graph Neural Networks | 2025 | VLDB |
| 9 | 8,586 | A Framework for Privacy Preserving Localized Graph Pattern Query Processing | 2023 | SIGMOD |
| 10 | 5,384 | Privacy Preserving Subgraph Matching on Large Graphs in Cloud | 2016 | SIGMOD |