Efficient parallel summation on encrypted database system

Kentaro Horio, Hideyuki Kawashima, Osamu Tatebe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The spread of cloud computing services has resulted in data storage and data management services being outsourced to servers operated by a third party. This outsourcing can raise some security concerns because the administrators of the servers may snoop and explore the data. Encrypted database systems provide a certain level of security by encrypting the data they contain. It provides relational operators over encrypted data, which does not require data decryption and blocks the incidents. The disadvantage of this scheme is the considerable execution expense associated with data processing. Current encrypted database systems do not provide sufficient performance improvement. We focus on the summation operator in this paper, and we propose a parallel execution scheme of sum aggregation over the encrypted database. The proposed method employs task parallelism, and we assign a fine-grained data objects for each thread. We evaluate the proposed method on our prototype database system implemented in C++ and OpenMP. The result showed that proposed method achieves 15.99× performance improvement compared with a conventional method.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages178-185
Number of pages8
ISBN (Electronic)9781509030156
DOIs
Publication statusPublished - 2017 Mar 17
Externally publishedYes
Event2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 - Jeju Island, Korea, Republic of
Duration: 2017 Feb 132017 Feb 16

Other

Other2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
CountryKorea, Republic of
CityJeju Island
Period17/2/1317/2/16

Fingerprint

Servers
Storage management
Outsourcing
Cloud computing
Information management
Agglomeration

Keywords

  • Data Security
  • Database Systems
  • Encryption
  • Query Processing

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Horio, K., Kawashima, H., & Tatebe, O. (2017). Efficient parallel summation on encrypted database system. In 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 (pp. 178-185). [7881735] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2017.7881735

Efficient parallel summation on encrypted database system. / Horio, Kentaro; Kawashima, Hideyuki; Tatebe, Osamu.

2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 178-185 7881735.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Horio, K, Kawashima, H & Tatebe, O 2017, Efficient parallel summation on encrypted database system. in 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017., 7881735, Institute of Electrical and Electronics Engineers Inc., pp. 178-185, 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017, Jeju Island, Korea, Republic of, 17/2/13. https://doi.org/10.1109/BIGCOMP.2017.7881735
Horio K, Kawashima H, Tatebe O. Efficient parallel summation on encrypted database system. In 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 178-185. 7881735 https://doi.org/10.1109/BIGCOMP.2017.7881735
Horio, Kentaro ; Kawashima, Hideyuki ; Tatebe, Osamu. / Efficient parallel summation on encrypted database system. 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 178-185
@inproceedings{3d08a2679cc64d82aec8c6fd71d282e9,
title = "Efficient parallel summation on encrypted database system",
abstract = "The spread of cloud computing services has resulted in data storage and data management services being outsourced to servers operated by a third party. This outsourcing can raise some security concerns because the administrators of the servers may snoop and explore the data. Encrypted database systems provide a certain level of security by encrypting the data they contain. It provides relational operators over encrypted data, which does not require data decryption and blocks the incidents. The disadvantage of this scheme is the considerable execution expense associated with data processing. Current encrypted database systems do not provide sufficient performance improvement. We focus on the summation operator in this paper, and we propose a parallel execution scheme of sum aggregation over the encrypted database. The proposed method employs task parallelism, and we assign a fine-grained data objects for each thread. We evaluate the proposed method on our prototype database system implemented in C++ and OpenMP. The result showed that proposed method achieves 15.99× performance improvement compared with a conventional method.",
keywords = "Data Security, Database Systems, Encryption, Query Processing",
author = "Kentaro Horio and Hideyuki Kawashima and Osamu Tatebe",
year = "2017",
month = "3",
day = "17",
doi = "10.1109/BIGCOMP.2017.7881735",
language = "English",
pages = "178--185",
booktitle = "2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Efficient parallel summation on encrypted database system

AU - Horio, Kentaro

AU - Kawashima, Hideyuki

AU - Tatebe, Osamu

PY - 2017/3/17

Y1 - 2017/3/17

N2 - The spread of cloud computing services has resulted in data storage and data management services being outsourced to servers operated by a third party. This outsourcing can raise some security concerns because the administrators of the servers may snoop and explore the data. Encrypted database systems provide a certain level of security by encrypting the data they contain. It provides relational operators over encrypted data, which does not require data decryption and blocks the incidents. The disadvantage of this scheme is the considerable execution expense associated with data processing. Current encrypted database systems do not provide sufficient performance improvement. We focus on the summation operator in this paper, and we propose a parallel execution scheme of sum aggregation over the encrypted database. The proposed method employs task parallelism, and we assign a fine-grained data objects for each thread. We evaluate the proposed method on our prototype database system implemented in C++ and OpenMP. The result showed that proposed method achieves 15.99× performance improvement compared with a conventional method.

AB - The spread of cloud computing services has resulted in data storage and data management services being outsourced to servers operated by a third party. This outsourcing can raise some security concerns because the administrators of the servers may snoop and explore the data. Encrypted database systems provide a certain level of security by encrypting the data they contain. It provides relational operators over encrypted data, which does not require data decryption and blocks the incidents. The disadvantage of this scheme is the considerable execution expense associated with data processing. Current encrypted database systems do not provide sufficient performance improvement. We focus on the summation operator in this paper, and we propose a parallel execution scheme of sum aggregation over the encrypted database. The proposed method employs task parallelism, and we assign a fine-grained data objects for each thread. We evaluate the proposed method on our prototype database system implemented in C++ and OpenMP. The result showed that proposed method achieves 15.99× performance improvement compared with a conventional method.

KW - Data Security

KW - Database Systems

KW - Encryption

KW - Query Processing

UR - http://www.scopus.com/inward/record.url?scp=85017630608&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85017630608&partnerID=8YFLogxK

U2 - 10.1109/BIGCOMP.2017.7881735

DO - 10.1109/BIGCOMP.2017.7881735

M3 - Conference contribution

AN - SCOPUS:85017630608

SP - 178

EP - 185

BT - 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -