AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data
Summary: AIM is a workload-adaptive DP synthetic-data generator that iteratively selects measurements, privately measures them, and yields data from noisy results. It links measurement choice to workload relevance and data-approximation, provides high-probability per-query error bounds, and outperforms existing DP mechanisms. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ryan McKenna
- 2. Brett Mullins
- 3. Daniel Sheldon
- 4. Gerome Miklau
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,485 | Differentially Private Data Generation with Missing Data | 2024 | VLDB | 4.7135369e-05 |
| 8,275 | Synthetic Tabular Data: Methods, Attacks and Defenses | 2025 | VLDB | 4.5392079e-05 |
| 9,513 | Answering Private Linear Queries Adaptively using the Common Mechanism | 2023 | VLDB | 4.3294349e-05 |
| 10,053 | Benchmarking Differentially Private Tabular Data Synthesis: [Experiments & Analysis] | 2026 | SIGMOD | 4.1905499e-05 |
| 10,509 | PrivPetal: Relational Data Synthesis via Permutation Relations | 2025 | SIGMOD | 4.1905499e-05 |
| 10,731 | Privacy-Enhanced Database Synthesis for Benchmark Publishing | 2025 | VLDB | 4.1905499e-05 |
| 10,913 | Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections | 2024 | PODS | 4.1905499e-05 |
| 11,146 | DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms | 2024 | VLDB | 4.1905499e-05 |
| 11,262 | Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy | 2023 | VLDB | 4.1905499e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 137 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00042381562 |
| 872 | Differentially Private Data Cubes: Optimizing Noise Sources and Consistency | 2011 | SIGMOD | 0.00015702532 |
| 2,436 | Optimizing error of high-dimensional statistical queries under differential privacy | 2018 | VLDB | 8.8217855e-05 |
| 2,461 | Principled Evaluation of Differentially Private Algorithms using DPBench | 2016 | SIGMOD | 8.7486072e-05 |
| 2,889 | Data Synthesis via Differentially Private Markov Random Fields | 2021 | VLDB | 7.9589189e-05 |
| 3,306 | Plausible Deniability for Privacy-Preserving Data Synthesis | 2017 | VLDB | 7.2411112e-05 |
| 3,834 | Kamino: Constraint-Aware Differentially Private Data Synthesis | 2021 | VLDB | 6.713066e-05 |
| 7,501 | PSynDB: Accurate and Accessible Private Data Generation | 2019 | VLDB | 4.7135369e-05 |
Previous
Page 1 / 1
Next