I/O Performance Improvement of FHE Apriori with Striping File Layout Considering Storage of Intermediate Data
Author:
Affiliation:
1. Kogakuin University
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://www.jstage.jst.go.jp/article/transinf/E106.D/6/E106.D_2022EDL8071/_pdf
Reference5 articles.
1. [1] H. Imabayashi, Y. Ishimaki, A. Umayabara, and H. Yamana, “Fast and space-efficient secure frequent pattern mining by FHE,” 2016 IEEE International Conference on Big Data, pp.3983-3985, 2016. 10.1109/bigdata.2016.7841083
2. [2] E. Fujishima, K. Nakashima, and S. Yamaguchi, “Hadoop I/O Performance Improvement by File Layout Optimization,” IEICE Transactions on Information and Systems, vol.E101-D, no.2, pp.415-427, Feb. 2018. 10.1587/transinf.2017edp7114
3. [3] A. Kamo and S. Yamaguchi, “I/O Performance Improvement with Striping File Layout Considering Storage of Intermediate Data,” 2021 IEEE International Conference on Big Data, pp.5923-5925, 2021. 10.1109/bigdata52589.2021.9672053
4. [4] J. Kon, G.J. Lee, J.A.B. Fortes and S. Yamaguchi, “A Kernel-Based Method for Resolving Performance Inefficiencies in Mining Frequent-Patterns in Encrypted Data,” 2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW), pp.506-510, 2018. 10.1109/candarw.2018.00098
5. [5] H. Huang, W. Hung, and K.G. Shin, “FS2: dynamic data replication in free disk space for improving disk performance and energy consumption,” SIGOPS Oper. Syst. Rev., vol.39, no.5, pp.263-276, Dec. 2005. https://doi.org/10.1145/1095809.1095836
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