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Differentially Private Substring and Document Counting

Summary: DP for substring and document counting in document collections; epsilon-DP data structure yields additive error O(l polylog(n l |Sigma|)) for all patterns, optimal up to polylog. For epsilon-delta DP, bound improves to O(sqrt(l) polylog(n l |Sigma|)); space O(n l^2), preprocessing O(n^2 l^4), query O(|P|); introduces a tree-counting technique enabling private mining of frequent substrings and q-grams. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
1966
Venue
PODS
Year
2025
Pagerank
4.1905499e-05
Overall Rank
10,358 | 28.02%
DOI
10.1145/3725232

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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
451 Towards Practical Differential Privacy for SQL Queries 2018 VLDB 0.00022807098
567 Practical Privacy: The SuLQ Framework 2005 PODS 0.00019940193
1,516 PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions 2016 SIGMOD 0.00011558246
2,688 On Differentially Private Frequent Itemset Mining 2013 VLDB 8.3012815e-05
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