Secure Skyline Groups Queries on Encrypted Data on Cloud Platform
Yiping Teng, Yue Sun, Zhan Shi, Dongyue Jiang, Liang Zhao, Chunlong Fan · 2021
As an important category of skyline queries, the skyline groups query has drawn growing interest from the academic communities. For taking the benefit of cost savings, data owners are motivated to outsource the data storage and query processing to the public cloud to offload the massive data management. However, direct outsourcing may raise severe privacy issues. In this paper, we define and study the secure skyline groups query problem, and propose the secure skyline groups query methods. To address this problem, we first propose the Basic Secure Skyline Groups Query (BSSGQ) method to find the skyline groups on encrypted data. In BSSGQ method, candidate groups and their aggregate tuples are generated under encryption. To calculate group dominance relations, we propose a secure group dominance protocol to facilitate the dominance computation between groups. With the dominance relations, the skyline groups w.r.t the query group can be obtained, and finally the query results of the skyline groups can be securely returned without being learnt by the cloud servers. To address the efficiency problem in BSSGQ method, we further propose a Dynamic-programming-based Secure Skyline Groups Query (DSSGQ) method to effectively avoid generating the candidate groups. In DSSGQ method, the encrypted data are equivalently mapped based on the query group as the preprocessing to achieve a dynamic skyline groups query. Then, DSSGQ method dynamically constructs and calculates the skyline sub-groups in ciphertext, and the final skyline groups can be combined with the obtained skyline sub-groups. Thorough analysis shows the security and complexity of the proposed methods, and extensive experimental results on real datasets and synthetic datasets illustrate the performance of our proposed methods.