SecSky: A Secure Dynamic Skyline Query Scheme With Data Privacy

Abstract

Skyline computation is a typical multi-objective optimization problem and has been a hot spot for current research. Most of them have concentrated on improving the efficiency of skyline computing, while the security issues associated with executing skyline computation in the cloud server are rarely taken into account. The cloud data stored in plaintext form is attacked by hackers or malicious administrators, resulting in potential leakage of users' private information. One of the most effective approaches to solve this problem is to encrypt the private data before storing it in the cloud server. However, how to compute the skyline on the encrypted data stored on a cloud server efficiently is still challenging. In this paper, we present a novel framework called SecSky. Firstly, the improved B+ tree structure and symmetric encryption were combined to construct a secure storage structure. Then, the pruning idea was applied to reduce unnecessary calculation and to achieve efficient skyline query and dynamic update. The whole process was directly completed on the ciphertext data. Finally, both the security and the efficiency of SecSky were analyzed through theoretical proof and simulation experiments. The results showed that our scheme is of sound efficiency on the premise of ensuring private data security.

Keywords

• Skyline query
• security
• dynamic update
• cloud