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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. [ABSTRACT FROM AUTHOR]