跨信任域的联邦k-支配Sk yline查询算法

史烨轩; 童咏昕; 周昊; 许可; 吕卫锋

doi:10.11959/j.issn.2096-0271.2023047

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跨信任域的联邦k-支配Sk yline查询算法

专题：跨域数据管理 | 更新时间：2024-06-03

- 跨信任域的联邦k-支配Sk yline查询算法
- Cross trust domain federated k-dominant skyline query processing
- 大数据 2023年9卷第4期页码：32-43
- 作者机构：
  
  1. 北京航空航天大学软件开发环境国家重点实验室，北京 100191
  2. 北京航空航天大学计算机学院，北京 100191
  3. 北京航空航天大学未来区块链与隐私计算高精尖创新中心，北京 100191
- 作者简介：
  
  [ "史烨轩（1994- ），男，博士，北京航空航天大学计算机学院博士后，主要研究方向为大数据分析处理、联邦学习和隐私计算" ]
  [ "童咏昕（1982- ），男，北京航空航天大学计算机学院教授，主要研究方向为联邦学习、隐私计算、时空大数据分析、数据库技术和群体智能" ]
  [ "周昊（1999- ），男，北京航空航天大学计算机学院硕士生，主要研究方向为大数据分析处理、联邦学习和隐私计算" ]
  [ "许可（1971- ），男，北京航空航天大学计算机学院教授，主要研究方向为算法与复杂性、数据挖掘和群体智能等" ]
  [ "吕卫锋（1972- ），男，北京航空航天大学计算机学院教授，北京航空航天大学副校长，软件开发环境国家重点实验室副主任，主要研究方向为时空大数据分析、智慧城市和群体智能等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(U21A20516);国家自然科学基金资助项目;The National Natural Science Foundation of China(62076017);北航基础研究建设基金资助项目;Beihang University Basic Research Funding(YWF-22-L-531);微众学者计划;WeBank Scholars Program
- DOI：10.11959/j.issn.2096-0271.2023047
  中图分类号： TP391
- 网络首发：2023-07，
  
  纸质出版：2023-07-15
- 稿件说明：
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史烨轩, 童咏昕, 周昊, 等. 跨信任域的联邦k-支配Sk yline查询算法[J]. 大数据, 2023,9(4):32-43.

Yexuan SHI, Yongxin TONG, Hao ZHOU, et al. Cross trust domain federated k-dominant skyline query processing[J]. Big data research, 2023, 9(4): 32-43.
史烨轩, 童咏昕, 周昊, 等. 跨信任域的联邦k-支配Sk yline查询算法[J]. 大数据, 2023,9(4):32-43. DOI： 10.11959/j.issn.2096-0271.2023047.

Yexuan SHI, Yongxin TONG, Hao ZHOU, et al. Cross trust domain federated k-dominant skyline query processing[J]. Big data research, 2023, 9(4): 32-43. DOI： 10.11959/j.issn.2096-0271.2023047.

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。

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.

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references

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