Association Rule Mining with Differential Privacy

Hao Zhen, Bo Cheng Chiou, Yao Tung Tsou*, Sy Yen Kuo, Pang Chieh Wang

*此作品的通信作者

研究成果: 圖書/報告稿件的類型會議稿件同行評審

2 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題Proceedings - 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面47-54
頁數8
ISBN(電子)9781728172637
DOIs
出版狀態已出版 - 06 2020
對外發佈
事件50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2020 - Valencia, 西班牙
持續時間: 29 06 202002 07 2020

出版系列

名字Proceedings - 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2020

Conference

Conference50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2020
國家/地區西班牙
城市Valencia
期間29/06/2002/07/20

文獻附註

Publisher Copyright:
© 2020 IEEE.

指紋

深入研究「Association Rule Mining with Differential Privacy」主題。共同形成了獨特的指紋。

引用此