Paper Info
Title
Association Rule Mining with Differential Privacy
Abstract
Association analysis is an important task in data analysis to find all co-occurrence relationships (i.e., frequent itemsets or confident association rules) from the transactional dataset. An association rule can help people better discover patterns and develop corresponding strategies. The process of data analysis can be highly summarized as a set of queries, where each query is a real-valued function of the dataset. However, without any restriction and protection, accessing the dataset to answer the queries may lead to the disclosure of individual privacy. In this paper, we propose and implement the association rule mining with differential privacy algorithm, which uses multiple support thresholds to reduce the number of candidate itemsets while reflecting the real nature of the items, and uses random truncation and uniform partition to lower the dimensionality of the dataset. We also stabilize the noise scale by adaptively allocating the privacy budgets, and bound the overall privacy loss. In addition, we prove that the association rule mining with differential privacy algorithm satisfies ex post differential privacy, and verify the utility of our association rule mining with differential privacy algorithm through a series of experiments. To the best of our knowledge, our work is the first differentially private association rule mining algorithm under multiple support thresholds.
Year
DOI
Venue
2020
10.1109/DSN-W50199.2020.00017
2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)
Keywords
DocType
ISSN
Differential privacy,individual privacy,association analysis,association rule mining,frequent itemset mining
Conference
2325-6648
ISBN
Citations 
PageRank 
978-1-7281-7264-4
0
0.34
References 
Authors
15
5
Name
Order
Citations
PageRank
Hao Zhen100.68
Bo-Cheng Chiou200.34
Yao-Tung Tsou3475.36
Sy-Yen Kuo42304245.46
Pang-Chieh Wang511.38