A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy
Summary: Neural approach uses a variational auto-encoder to sanitize spatio-temporal data under user-level DP, reducing DP noise without sacrificing utility. Extensive experiments on real data show superior accuracy and privacy tradeoffs versus benchmarks and high-budget releases. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ritesh Ahuja
- 2. Sepanta Zeighami
- 3. Gabriel Ghinita
- 4. Cyrus Shahabi
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,107 | NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks | 2023 | SIGMOD | 4.3950706e-05 |
| 10,390 | RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning | 2025 | SIGMOD | 4.1945683e-05 |
| 10,664 | Calibrating Noise for Group Privacy in Subsampled Mechanisms | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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