保护隐私的图表 机器学习从数据到计算:一项调查
Dongqi Fu1, Wenxuan Bao1, Ross Maciejewski2
1University of Illinois Urbana-Champaign.
概括
本综述探讨了图形机器学习 (GML) 中的隐私保护技术. 它涵盖数据生成,安全信息传输和计算方法,以保护复杂网络中的敏感数据.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 在图形机器学习 (GML) 中,数据收集,共享和分析涉及多个具有不同安全需求的各方.
- 保护隐私对于保护复杂的大数据网络中的敏感信息至关重要.
- 图形数据结构和基于图形的AI模型,如图形神经网络,越来越多地用于各种领域.
研究的目的:
- 系统地审查图形机器学习中的现有隐私保护技术.
- 从数据生成和计算方面对方法进行分类和分析.
- 确定安全的GML当前的挑战和未来的研究方向.
主要方法:
- 关于GML中隐私保护方法的全面文献综述.
- 基于数据生成和信息传输的技术的分类.
- 分析理论方法,软件工具和实际应用.
主要成果:
- 审查了生成隐私保护图形数据的方法.
- 描述了用于分布式计算的图形模型参数安全传输的技术.
- 讨论包括理论基础,软件工具和现场挑战.
结论:
- 该审查提供了对保护隐私的GML技术的结构化概述.
- 确定了挑战和未来的研究机会,旨在推进安全的GML系统.
- 设想一个统一和全面的安全图形机器学习系统.
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