Efficient Location-Based Skyline Queries With Secure R-Tree Over Encrypted Data

Supporting efficient and secure location-based skyline queries on encrypted data, such as private data outsourced to cloud-based systems, remains an ongoing challenge for efficiency due to significant computational costs in the ciphertext domain. To accelerate privacy-preserving skyline queries, the secure index intuitively contributes to an increase in efficiency. However, designing such a secure index is a challenge while protecting the unlinkability of queries. Meanwhile, there exist little work that can commendably assure efficiency and security. In this paper, we demonstrate SecSky, an efficient solution for supporting secure location-based skyline queries through the secure index. To support SecSky, we devise a novel unified structure, named secure R-tree (SR-tree) index, without privacy leakage (especially indirect privacy). Subsequently, we propose a novel secure location-based dominance protocol, which is utilized to calculate the dominance relationship on the SR-tree. Using this protocol as the building block, our secure location-based skyline query protocol integrates SR-tree, permutation and perturbation techniques to facilitate query processing so as to dramatically reduce the computational overhead. Meanwhile, our proposed solution avoids compromising the privacy of datasets, queries, dominance relationship and skyline results. Finally, we analyze the complexity and security of SecSky. Findings from the experimental evaluation show that our proposed scheme outperforms several other protocols by at least 3 orders of magnitude in terms of query efficiency.

Automated summary

This paper presents SecSky, an efficient solution for supporting secure location-based skyline queries. The solution utilizes a secure index and a novel secure protocol to improve query efficiency and protect privacy.