Efficient and Secure Skyline Query Over Horizontal Data Federation

Yilun Kuang, An Liu, Jianfeng Qu, Junhua Fang, Xiaofang Zhang, Lei Zhao

Abstract: Skyline is a primitive operation in multi-criteria decision applications and there is an increasing demand to support such operations over a data federation, where the entire dataset is separately held by multiple data providers (a.k.a., silos), and each silo keeps its data partition private. Yet such federations also challenge the conventional implementation of skyline queries because the raw data cannot be shared within the federation and the secure computation cross silos can be two or three orders of magnitude slower than plaintext computation, etc. These constraints render existing solutions inefficient on data federation. In this work, we propose a novel scheme for efficient skyline queries over a horizontal data federation. We decompose the skyline query into plaintext computations and secure multi-party computations, enabling more plaintext computations without compromising security. We further propose an acceleration scheme that combines tighten predefined bound methods to accelerate our query process. We theoretically analyze their communication cost and time complexity. Finally, we empirically study the schemes in terms of efficiency and scalability under different parameter settings, verifying the feasibility of our proposed solutions.

External IDs:dblp:conf/dasfaa/KuangLQFZZ24