Practical Differential Privacy via Grouping and Smoothing
Summary: GS pre-processes one-time, non-overlapping counts by grouping and smoothing (averaging) to lower sensitivity before epsilon-DP noise. A sampling-guided grouping mechanism minimizes smoothing perturbation, enabling low-noise DP and real-data superiority over competitors, with extensive experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Georgios Kellaris (Hong Kong University of Science and Technology)
- 2. Stavros Papadopoulos (Hong Kong University of Science and Technology)
BibTeX Citation
@article{kellaris_vldb13,
title = {{Practical Differential Privacy via Grouping and Smoothing}},
author = {Kellaris, Georgios and Papadopoulos, Stavros},
journal = {PVLDB},
series = {{VLDB} '13},
volume = {6},
number = {5},
doi = {10.14778/2535573.2488337},
url = {https://doi.org/10.14778/2535573.2488337},
year = {2013}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,838 | Publishing Graph Degree Distribution with Node Differential Privacy | 2016 | SIGMOD | 9.5315245e-05 |
| 6,229 | The Fast and the Private: Task-based Dataset Search | 2024 | CIDR | 5.8444773e-05 |
| 6,335 | A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy | 2023 | SIGMOD | 5.8100198e-05 |
| 7,558 | Doquet: Differentially Oblivious Range and Join Queries with Private Data Structures | 2023 | VLDB | 5.4964611e-05 |
| 8,133 | Optimizing Fitness-For-Use of Differentially Private Linear Queries | 2021 | VLDB | 5.3931029e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 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 | 0.00031089378 |
| 347 | Generalizing Data to Provide Anonymity when Disclosing Information | 1998 | PODS | 0.00020325683 |
| 625 | Optimizing Linear Counting Queries Under Differential Privacy | 2010 | PODS | 0.00015478765 |
| 732 | Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption | 2010 | SIGMOD | 0.00014383875 |
| 1,989 | Low-Rank Mechanism: Optimizing Batch Queries under Differential Privacy | 2012 | VLDB | 9.246325e-05 |
| 2,453 | Personalized Privacy Preservation | 2006 | SIGMOD | 8.4435151e-05 |
| 2,855 | iReduct: Differential Privacy with Reduced Relative Errors | 2011 | SIGMOD | 7.9391282e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,164 | Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation | 2008 | SIGMOD |
| 2 | 2,912 | Publishing Set-Valued Data via Differential Privacy | 2011 | VLDB |
| 3 | 6,210 | Global and Local Differentially Private Release of Count-Weighted Graphs | 2023 | SIGMOD |
| 4 | 10,639 | Enhancing Local Differential Privacy Accuracy by Exploiting Inherent Uncertainty | 2026 | SIGMOD |
| 5 | 2,803 | Estimating Numerical Distributions under Local Differential Privacy | 2020 | SIGMOD |
| 6 | 1,528 | A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy | 2014 | VLDB |
| 7 | 10,539 | A General Framework for Per-record Differential Privacy | 2026 | SIGMOD |
| 8 | 2,118 | Differentially Private Event Sequences over Infinite Streams | 2014 | VLDB |
| 9 | 11,544 | Personalized Truncation for Personalized Privacy | 2024 | SIGMOD |
| 10 | 3,993 | Output Perturbation with Query Relaxation | 2008 | VLDB |