【摘要】 k-支配Skyline查询是一种主流的Skyline查询变种，其在多目标决策与推荐领域有着广泛的应用。随着这些应用规模不断扩大，在由多个参与方组成的数据联邦中进行跨域k-支配Skyline查询的需求日益旺盛。然而，由于数据联邦中的参与方之间彼此不互信，进行跨信任域的查询计算需引入大量安全操作，效率较低。为此提出了一种基于跨域隐私向量聚合的算法，从而实现高效的联邦k-支配Skyline查询，并运用一种密文压缩技术进一步优化查询效率，最后通过充分的实验验证了所提方案的优越性。更多还原

【Abstract】 k-dominant skyline is a prevailing skyline query which has widespread applications in multi-criteria decision making and recommendation. As these applications continuously scale up, there is an increasing demand to support k-dominant skyline over a data federation which consists of multiple data silos, each holding disjoint columns of the entire dataset. Yet it is challenging to support k-dominant skyline over a data federation. This is because strict security constraints are often imposed to query processing over data federations, whereas naively adopting security techniques leads to unacceptably inefficient queries. In this paper, we presented an efficient and secure k-dominant skyline for a data federation. Specifically, we devised a novel private vector aggregation-based solution with ciphertext compressionbased optimization for efficient k-dominant skyline query processing while providing security guarantees. Extensive evaluations on both synthetic and real datasets showed the superiority of our method.更多还原