Mining Frequent Patterns with Differential Privacy

Summary: Differential privacy for mining frequent patterns; defines exact vs. noisy patterns in itemsets and sequences. Two exact-pattern methods: privacy-preserving record linkage; a two-phase substring/prefix mining method; plus a noisy-pattern taxonomy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 10628
Venue: VLDB
Year: 2013
Pagerank: 5.3322378e-05
Overall Rank: 5,772 | 59.85%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Luca Bonomi

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,410	Publishing Attributed Social Graphs with Formal Privacy Guarantees	2016	SIGMOD	5.0753667e-05
7,484	Privacy Amplification via Shuffling: Unified, Simplified, and Tightened	2024	VLDB	4.7180617e-05
8,234	Robust Privacy-Preserving Triangle Counting under Edge Local Differential Privacy	2025	SIGMOD	4.5535352e-05
9,766	DPXPlain: Privately Explaining Aggregate Query Answers	2023	VLDB	4.2856106e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
13	Mining Association Rules between Sets of Items in Large Databases	1993	SIGMOD	0.0010864752
1,567	PrivBasis: Frequent Itemset Mining with Differential Privacy	2012	VLDB	0.0001133268
2,685	On Differentially Private Frequent Itemset Mining	2013	VLDB	8.3070708e-05
4,537	Privacy Preserving Schema and Data Matching	2007	SIGMOD	6.1042536e-05

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181	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00036992674
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1,567	PrivBasis: Frequent Itemset Mining with Differential Privacy	2012	VLDB	0.0001133268
2,685	On Differentially Private Frequent Itemset Mining	2013	VLDB	8.3070708e-05