TY - GEN
T1 - Secure Outsourced Top-k Selection Queries against Untrusted Cloud Service Providers
AU - Yu, Xixun
AU - Hu, Yidan
AU - Zhang, Rui
AU - Yan, Zheng
AU - Zhang, Yanchao
N1 - Publisher Copyright: © 2021 IEEE.
PY - 2021/6/25
Y1 - 2021/6/25
N2 - As cloud computing reshapes the global IT industry, an increasing number of business owners have outsourced their datasets to third-party cloud service providers (CSP), which in turn answer data queries from end users on their behalf. A well known security challenge in data outsourcing is that the CSP cannot be fully trusted, which may return inauthentic or unsound query results for various reasons. This paper considers top-k selection queries, an important type of queries widely used in practice. In a top-k selection query, a user specifies a scoring function and asks for the k objects with the highest scores. Despite several recent efforts, existing solutions can only support a limited range of scoring functions with explicit forms known in advance. This paper presents three novel schemes that allow a user to verify the integrity and soundness of any top-k selection query result returned by an untrusted CSP. The first two schemes support monotone scoring functions, and the third scheme supports scoring functions comprised of both monotonically non-decreasing and non-increasing subscoring functions. Detailed simulation studies using a real dataset confirm the efficacy and efficiency of the proposed schemes and their significant advantages over prior solutions.
AB - As cloud computing reshapes the global IT industry, an increasing number of business owners have outsourced their datasets to third-party cloud service providers (CSP), which in turn answer data queries from end users on their behalf. A well known security challenge in data outsourcing is that the CSP cannot be fully trusted, which may return inauthentic or unsound query results for various reasons. This paper considers top-k selection queries, an important type of queries widely used in practice. In a top-k selection query, a user specifies a scoring function and asks for the k objects with the highest scores. Despite several recent efforts, existing solutions can only support a limited range of scoring functions with explicit forms known in advance. This paper presents three novel schemes that allow a user to verify the integrity and soundness of any top-k selection query result returned by an untrusted CSP. The first two schemes support monotone scoring functions, and the third scheme supports scoring functions comprised of both monotonically non-decreasing and non-increasing subscoring functions. Detailed simulation studies using a real dataset confirm the efficacy and efficiency of the proposed schemes and their significant advantages over prior solutions.
UR - https://www.scopus.com/pages/publications/85115384716
UR - https://www.scopus.com/pages/publications/85115384716#tab=citedBy
U2 - 10.1109/IWQOS52092.2021.9521321
DO - 10.1109/IWQOS52092.2021.9521321
M3 - Conference contribution
T3 - 2021 IEEE/ACM 29th International Symposium on Quality of Service, IWQOS 2021
BT - 2021 IEEE/ACM 29th International Symposium on Quality of Service, IWQOS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021
Y2 - 25 June 2021 through 28 June 2021
ER -