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 -