Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CLOUD.2019.00086 | 2019 IEEE 12th International Conference on Cloud Computing (CLOUD) |
Keywords | Field | DocType |
association rule mining,Apriori algorithm,encrypted data,cloud computing,Elgamal cryptosystem | Computer science,Elgamal cryptosystem,Apriori algorithm,Algorithm,Encryption,Association rule learning,Information privacy,Cloud computing | Conference |
ISSN | ISBN | Citations |
2159-6182 | 978-1-7281-2706-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyeong-Jin Kim | 1 | 9 | 4.25 |
| Jae-Hwan Shin | 2 | 0 | 0.68 |
| Youngho Song | 3 | 1 | 1.70 |
| Jae-Woo Chang | 4 | 401 | 99.85 |