RM2: Answer Counting Queries Efficiently under Shuffle Differential Privacy
Summary: RM2 implements the matrix mechanism under shuffle DP, minimizing per-user message complexity. An improved shuffle-DP mechanism lowers messages while keeping central-DP-like accuracy for range queries and data cubes, beating the naive baseline. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Qiyao Luo (Ant Financial)
- 2. Jianzhe Yu (Hong Kong University of Science and Technology)
- 3. Wei Dong (Nanyang Technological University)
- 4. Quanqing Xu (Ant Financial)
- 5. Chuanhui Yang (Ant Financial)
- 6. Ke Yi (Hong Kong University of Science and Technology)
BibTeX Citation
@inproceedings{luo_sigmod25,
title = {{RM2: Answer Counting Queries Efficiently under Shuffle Differential Privacy}},
author = {Luo, Qiyao and Yu, Jianzhe and Dong, Wei and Xu, Quanqing and Yang, Chuanhui and Yi, Ke},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725415},
url = {https://dl.acm.org/doi/10.1145/3725415},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,147 | Analysis of Shuffling Beyond Pure Local Differential Privacy | 2026 | PODS | 5.093636e-05 |
| 10,335 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD | 5.093636e-05 |
| 10,442 | Defense against Poisoning Attacks under Shuffle-DP | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 121 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00031639377 |
| 130 | Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release | 2007 | PODS | 0.00030604781 |
| 567 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB | 0.00016420715 |
| 611 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS | 0.00015747402 |
| 775 | Differentially Private Data Cubes: Optimizing Noise Sources and Consistency | 2011 | SIGMOD | 0.00014110531 |
| 2,108 | An Adaptive Mechanism for Accurate Query Answering under Differential Privacy | 2012 | VLDB | 9.1553397e-05 |
| 3,121 | Answering Range Queries Under Local Differential Privacy | 2019 | VLDB | 7.7357038e-05 |
| 4,132 | Answering Multi-Dimensional Range Queries under Local Differential Privacy | 2021 | VLDB | 6.8832571e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,147 | Analysis of Shuffling Beyond Pure Local Differential Privacy | 2026 | PODS |
| 2 | 9,603 | Multi-Analyst Differential Privacy for Online Query Answering | 2023 | VLDB |
| 3 | 8,489 | Network Shuffling: Privacy Amplification via Random Walks | 2022 | SIGMOD |
| 4 | 611 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS |
| 5 | 1,504 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB |
| 6 | 2,108 | An Adaptive Mechanism for Accurate Query Answering under Differential Privacy | 2012 | VLDB |
| 7 | 10,517 | Doppio: Communication-Efficient and Secure Multi-Party Shuffle Differential Privacy | 2026 | VLDB |
| 8 | 1,956 | Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy | 2012 | VLDB |
| 9 | 5,595 | Privacy Amplification via Shuffling: Unified, Simplified, and Tightened | 2024 | VLDB |
| 10 | 10,442 | Defense against Poisoning Attacks under Shuffle-DP | 2026 | SIGMOD |