Understanding the Sparse Vector Technique for Differential Privacy
Summary: Analyzes SVT variants for differential privacy, exposing flaws and misunderstandings across interactive and non-interactive settings. Proposes a tighter SVT with improved utility; in non-interactive DP, EM outperforms SVT, while interactive gains vary. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Min Lyu
- 2. Dong Su
- 3. Ninghui Li
Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 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 |
| 1,446 | PrivBayes: Private Data Release via Bayesian Networks | 2014 | SIGMOD | 0.00011931212 |
| 1,516 | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | 2016 | SIGMOD | 0.00011558246 |
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