DBScholar

Back to papers

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
h47f98e65644f851a
Venue
VLDB
Year
2013
Pagerank
6.0432078e-05
Overall Rank
5,673 | 61.86%
DOI
10.14778/2536274.2536329

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{bonomi_vldb13,
        title = {{Mining Frequent Patterns with Differential Privacy}},
        author = {Bonomi, Luca},
        journal = {PVLDB},
        series = {{VLDB} '13},
        volume = {6},
        number = {12},
        doi = {10.14778/2536274.2536329},
        url = {https://doi.org/10.14778/2536274.2536329},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Previous Page 1 / 1 Next

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 0.00064420972
1,453 PrivBasis: Frequent Itemset Mining with Differential Privacy 2012 VLDB 0.0001060277
2,416 On Differentially Private Frequent Itemset Mining 2013 VLDB 8.4962257e-05
5,037 Privacy Preserving Schema and Data Matching 2007 SIGMOD 6.3032648e-05
Previous Page 1 / 1 Next

Semantically Similar Papers