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 - Funding Information:
ACKNOWLEDGEMENT The authors would like to thank the anonymous reviewers for their constructive comments and helpful advice. This work was partially supported by National Natural Science Foundation of China under Grant 62072351, Academy of Finland under Grant 308087 and Grant 335262, US National Science Foundation through grants CNS-1933069, CNS-1824355, CNS-1651954 (CAREER), CNS-1718078 and CNS-1933047.
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.
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U2 - 10.1109/IWQOS52092.2021.9521321
DO - 10.1109/IWQOS52092.2021.9521321
M3 - Conference contribution
AN - SCOPUS:85115384716
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 -