@inproceedings{c9c0ae4f572c4027959598768bfce085,
title = "Privacy-preserving association rule mining algorithm for encrypted data in cloud computing",
abstract = "Recently, privacy-preserving association rules mining algorithms have been proposed to support data privacy. However, the algorithms have an additional overhead to insert fake items (or fake transactions) and cannot hide data frequency. In this paper, we propose a privacy-preserving association rule mining algorithm for encrypted data in cloud computing. For association rule mining, we utilize Apriori algorithm by using the Elgamal cryptosystem, without additional fake transactions. Thus the proposed algorithm can guarantee both data privacy and query privacy, while concealing data frequency. We show that the proposed algorithm achieves about 3-5 times better performance than the existing algorithm, in terms of association rule mining time.",
keywords = "Apriori algorithm, Association rule mining, Cloud computing, Elgamal cryptosystem, Encrypted data",
author = "Kim, \{Hyeong Jin\} and Shin, \{Jae Hwan\} and Song, \{Young Ho\} and Chang, \{Jae Woo\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 12th IEEE International Conference on Cloud Computing, CLOUD 2019 ; Conference date: 08-07-2019 Through 13-07-2019",
year = "2019",
month = jul,
doi = "10.1109/CLOUD.2019.00086",
language = "English",
series = "IEEE International Conference on Cloud Computing, CLOUD",
publisher = "IEEE Computer Society",
pages = "487--489",
editor = "Elisa Bertino and Chang, \{Carl K.\} and Peter Chen and Ernesto Damiani and Michael Goul and Katsunori Oyama",
booktitle = "Proceedings - 2019 IEEE International Conference on Cloud Computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services",
}