Secure Optimization with Preferred Skyline Predicate on Incomplete Data

Outsourcing data storage and computations to cloud servers offers a cost-effective solution for remote data management and query processing. However, ensuring the privacy of sensitive information remains a critical concern, and existing secure algorithms rely on data completeness where all attribute values are valid to ignore the dominance issues under intransitivity and cyclicity. This paper addresses the challenge of executing secure skyline predicates on outsourced incomplete data, while keeping the dataset, queries, and results confidential from the cloud servers. We propose a novel secure dominance under incomplete data as a core component of various query types. To balance security and efficiency, we introduce two filtering methods around access patterns. Additionally, we present two secure skyline extensions concerning dimension and skyband to produce meaningful skylines. The proposed solutions are empirically evaluated for efficiency and scalability on diverse datasets, demonstrating the practical viability of our approach.