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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)

Paper ID
11559
Venue
VLDB
Year
2017
Pagerank
8.1653216e-05
Overall Rank
2,758 | 80.82%
DOI
-

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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
136 Revealing Information while Preserving Privacy 2003 PODS 0.0004241101
1,446 PrivBayes: Private Data Release via Bayesian Networks 2014 SIGMOD 0.0001194108
1,520 PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions 2016 SIGMOD 0.00011535148
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