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On Differentially Private Frequent Itemset Mining

Summary: Analyzes differential privacy for frequent itemset mining; hardness arises from long transactions. Truncates long transactions to trade truncation error for DP noise, producing a practical classical itemset mining algorithm with better F-score than top-k methods, except at very small k; validated on benchmark datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10691
Venue
VLDB
Year
2013
Pagerank
8.3070708e-05
Overall Rank
2,685 | 81.33%
DOI
-

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