DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy
Summary: DPSAaS is a lightweight cloud middleware for multi-dimensional data sharing under local differential privacy. It encodes dimensions locally, enabling MDA queries with an analytics service that estimates results from encoded data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Min Xu (University of Chicago)
- 2. Tianhao Wang (Purdue University)
- 3. Bolin Ding (Alibaba)
- 4. Jingren Zhou (Alibaba)
- 5. Cheng Hong (Alibaba)
- 6. Zhicong Huang (Alibaba)
BibTeX Citation
@article{xu_vldb19,
title = {{DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy}},
author = {Xu, Min and Wang, Tianhao and Ding, Bolin and Zhou, Jingren and Hong, Cheng and Huang, Zhicong},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {12},
pages = {1862--1865},
doi = {10.14778/3352063.3352085},
url = {https://doi.org/10.14778/3352063.3352085},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,113 | CGM: An Enhanced Mechanism for Streaming Data Collection with Local Differential Privacy | 2021 | VLDB | 7.7415187e-05 |
| 5,595 | Privacy Amplification via Shuffling: Unified, Simplified, and Tightened | 2024 | VLDB | 6.1548101e-05 |
| 11,230 | AAA: an Adaptive Mechanism for Locally Differentially Private Mean Estimation | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 62 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00038970535 |
| 281 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB | 0.00022445849 |
| 1,220 | Calibrating Data to Sensitivity in Private Data Analysis: A Platform for Differentially-Private Analysis of Weighted Datasets | 2014 | VLDB | 0.00011619529 |
| 2,279 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.8155688e-05 |
| 7,147 | Architecting a Differentially Private SQL Engine | 2019 | CIDR | 5.6902859e-05 |
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