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 |
|---|---|---|---|---|
| 3,174 | CGM: An Enhanced Mechanism for Streaming Data Collection with Local Differential Privacy | 2021 | VLDB | 7.5678238e-05 |
| 5,726 | Privacy Amplification via Shuffling: Unified, Simplified, and Tightened | 2024 | VLDB | 6.0167127e-05 |
| 11,566 | AAA: an Adaptive Mechanism for Locally Differentially Private Mean Estimation | 2024 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 64 | Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis | 2009 | SIGMOD | 0.00038522486 |
| 276 | Towards Practical Differential Privacy for SQL Queries | 2018 | VLDB | 0.0002234348 |
| 1,240 | Calibrating Data to Sensitivity in Private Data Analysis: A Platform for Differentially-Private Analysis of Weighted Datasets | 2014 | VLDB | 0.00011378375 |
| 2,336 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD | 8.6206562e-05 |
| 7,295 | Architecting a Differentially Private SQL Engine | 2019 | CIDR | 5.5626111e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 7,793 | Budget Sharing for Multi-Analyst Differential Privacy | 2021 | VLDB |
| 2 | 5,868 | Federated Heavy Hitter Analytics with Local Differential Privacy | 2025 | SIGMOD |
| 3 | 8,945 | Differentially Private Stream Processing at Scale | 2024 | VLDB |
| 4 | 10,440 | DP-S4S: Accurate and Scalable Select-Join-Aggregate Query Processing with User-Level Differential Privacy | 2026 | SIGMOD |
| 5 | 1,097 | Privacy Preserving OLAP | 2005 | SIGMOD |
| 6 | 11,742 | On the Risks of Collecting Multidimensional Data Under Local Differential Privacy | 2023 | VLDB |
| 7 | 10,539 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 8 | 10,660 | MTSP-LDP: A Framework for Multi-Task Streaming Data Publication under Local Differential Privacy | 2026 | SIGMOD |
| 9 | 11,244 | Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services | 2025 | VLDB |
| 10 | 2,336 | Answering Multi-Dimensional Analytical Queries under Local Differential Privacy | 2019 | SIGMOD |