保护隐私的推系统使用分布式数据集的数据协作分析
Tomoya Yanagi1, Shunnosuke Ikeda1, Noriyoshi Sukegawa2
1Graduate School of Science and Technology, University of Tsukuba, Tsukuba-shi, Ibaraki, Japan.
PloS one
|April 21, 2025
概括
本研究介绍了推系统的隐私保护框架. 它通过实现分布式数据集的协作分析,同时保护机密信息,提高了评级预测的准确性.
科学领域:
- 计算机科学 计算机科学
- 数据 隐私 数据 隐私 数据
- 机器学习 机器学习
背景情况:
- 高质量的建议需要整合来自多方的分布式数据集.
- 分布式数据集的共享引发了人们对保护个人和机密信息的担忧.
- 现有的推系统往往缺乏强大的隐私保护机制,用于数据协作.
研究的目的:
- 建立一个保护隐私的推系统框架,使用分布式数据集的协作分析.
- 证明隐私保护技术在提高建议准确性的有效性.
- 为了实现安全的数据共享和集成,以增强用户建议.
主要方法:
- 为保护隐私的推系统开发一个新的框架.
- 使用分布式数据集上的数据协作分析.
- 使用两个公共评级数据集进行数值实验.
主要成果:
- 提出的隐私保护方法改善了分布式数据集的评级预测准确性.
- 与单个数据集分析相比,预测错误平均减少了4.5%至7.0%.
- 该框架在数据集成过程中成功保护了个人和机密信息.
结论:
- 在保护隐私的框架内,对分布式数据集的协作分析提高了推系统的性能.
- 开发的方法为推任务中安全共享数据提供了可行的解决方案.
- 这项研究为推系统领域的隐私保护技术开辟了新的途径.
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