Authenticating aggregate queries over set-valued data with confidentiality (extended abstract)

Cheng Xu; Qian Chen; Haibo HU; Jianliang XU; Xiaojun Hei

doi:10.1109/ICDE.2018.00256

Authenticating aggregate queries over set-valued data with confidentiality (extended abstract)

Cheng Xu, Qian Chen, Haibo HU, Jianliang XU, Xiaojun Hei

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

Abstract

With recent advances in data-As-A-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data.

Original language	English
Title of host publication	Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Publisher	IEEE
Pages	1797-1798
Number of pages	2
ISBN (Electronic)	9781538655207
DOIs	https://doi.org/10.1109/ICDE.2018.00256
Publication status	Published - 24 Oct 2018
Event	34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France Duration: 16 Apr 2018 → 19 Apr 2018 https://ieeexplore.ieee.org/xpl/conhome/8476188/proceeding

Publication series

Name	Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

Conference

Conference	34th IEEE International Conference on Data Engineering, ICDE 2018
Country/Territory	France
City	Paris
Period	16/04/18 → 19/04/18
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8476188/proceeding

Scopus Subject Areas

Information Systems
Information Systems and Management
Hardware and Architecture

User-Defined Keywords

Aggregate Quereis
Merkel Hash Tree
Query Authentication
Set Valued Data

Access to Document

10.1109/ICDE.2018.00256

Cite this

Xu, C., Chen, Q., HU, H., XU, J., & Hei, X. (2018). Authenticating aggregate queries over set-valued data with confidentiality (extended abstract). In Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 (pp. 1797-1798). [8509477] (Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018). IEEE. https://doi.org/10.1109/ICDE.2018.00256

@inproceedings{cef5c1ab01a2462db05bcbf638eaf69d,

title = "Authenticating aggregate queries over set-valued data with confidentiality (extended abstract)",

abstract = "With recent advances in data-As-A-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data.",

keywords = "Aggregate Quereis, Merkel Hash Tree, Query Authentication, Set Valued Data",

author = "Cheng Xu and Qian Chen and Haibo HU and Jianliang XU and Xiaojun Hei",

note = "Funding Information: ACKNOWLEDGEMENT This work was supported by Research Grants Council (RGC) of Hong Kong under GRF Projects 12244916, 15238116, 12202414, 12200114, 12200914, CRF Project C1008-16G, and NSFC Grant 61370231. Haibo Hu is also supported by PolyU Start-up Fund.; 34th IEEE International Conference on Data Engineering, ICDE 2018 ; Conference date: 16-04-2018 Through 19-04-2018",

year = "2018",

month = oct,

day = "24",

doi = "10.1109/ICDE.2018.00256",

language = "English",

series = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",

publisher = "IEEE",

pages = "1797--1798",

booktitle = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",

address = "United States",

url = "https://ieeexplore.ieee.org/xpl/conhome/8476188/proceeding",

}

Xu, C, Chen, Q, HU, H, XU, J & Hei, X 2018, Authenticating aggregate queries over set-valued data with confidentiality (extended abstract). in Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018., 8509477, Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018, IEEE, pp. 1797-1798, 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, 16/04/18. https://doi.org/10.1109/ICDE.2018.00256

Authenticating aggregate queries over set-valued data with confidentiality (extended abstract). / Xu, Cheng; Chen, Qian; HU, Haibo et al.

Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. IEEE, 2018. p. 1797-1798 8509477 (Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018).

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Authenticating aggregate queries over set-valued data with confidentiality (extended abstract)

AU - Xu, Cheng

AU - Chen, Qian

AU - HU, Haibo

AU - XU, Jianliang

AU - Hei, Xiaojun

N1 - Funding Information: ACKNOWLEDGEMENT This work was supported by Research Grants Council (RGC) of Hong Kong under GRF Projects 12244916, 15238116, 12202414, 12200114, 12200914, CRF Project C1008-16G, and NSFC Grant 61370231. Haibo Hu is also supported by PolyU Start-up Fund.

PY - 2018/10/24

Y1 - 2018/10/24

N2 - With recent advances in data-As-A-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data.

AB - With recent advances in data-As-A-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data.

KW - Aggregate Quereis

KW - Merkel Hash Tree

KW - Query Authentication

KW - Set Valued Data

UR - http://www.scopus.com/inward/record.url?scp=85057130825&partnerID=8YFLogxK

U2 - 10.1109/ICDE.2018.00256

DO - 10.1109/ICDE.2018.00256

M3 - Conference contribution

AN - SCOPUS:85057130825

T3 - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

SP - 1797

EP - 1798

BT - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

PB - IEEE

T2 - 34th IEEE International Conference on Data Engineering, ICDE 2018

Y2 - 16 April 2018 through 19 April 2018

ER -

Authenticating aggregate queries over set-valued data with confidentiality (extended abstract)

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

Access to Document

Other files and links

Fingerprint

Cite this