Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise
Summary: Revisits SVT privacy analysis by exploiting that SVT only releases binary exceedance bits, enabling a less-conservative DP accounting that admits exponential noise as optimal for perturbation. Introduces utility-driven threshold correction and appending strategies to counter exponential-noise bias, boosting precision/recall and improving query accuracy up to ~50% theoretically and empirically. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuhan Liu
- 2. Sheng Wang
- 3. Yixuan Liu
- 4. Feifei Li
- 5. Hong Chen
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,094 | N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics | 2026 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,758 | Understanding the Sparse Vector Technique for Differential Privacy | 2017 | VLDB | 8.1653216e-05 |
| 2,899 | Privacy at Scale: Local Differential Privacy in Practice | 2018 | SIGMOD | 7.9443198e-05 |
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| 7,997 | Optimizing Fitness-For-Use of Differentially Private Linear Queries | 2021 | VLDB | 4.6105691e-05 |
| 3,760 | Output Perturbation with Query Relaxation | 2008 | VLDB | 6.7805033e-05 |
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| 9,417 | Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms | 2020 | VLDB | 4.3441378e-05 |
| 2,758 | Understanding the Sparse Vector Technique for Differential Privacy | 2017 | VLDB | 8.1653216e-05 |