高质量和隐私保护的细粒度个性化数据聚合方案
Zhuoyue Xia1, Raja Kumar Murugesan1
1School of Computer Science, Taylor's University, Subang Jaya 47500, Malaysia.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了一个保护隐私的真相发现框架,用于移动人群传感 (MCS). 它可以实现准确的数据聚合,同时通过个性化,特定任务的权重和加密来保护用户位置和数据隐私.
科学领域:
- 计算机科学 计算机科学
- 无处不在的计算无处不在的计算
- 数据 隐私 数据 隐私 数据
背景情况:
- 移动人群传感 (MCS) 需要真相发现来聚合噪音数据.
- 现有的方法往往会损害用户隐私或缺乏特定任务的可靠性.
研究的目的:
- 为MCS开发一个保护隐私的真相发现框架.
- 为了实现高质量的数据聚合,同时保护用户位置和数据隐私.
- 支持细粒度,任务级激励措施.
主要方法:
- 一个任务明智的,个性化的,保护隐私的真相发现框架.
- 每个用户,每个任务的重量学习,用于聚合.
- 仅用于聚合处理的Paillier同态加密.
- 任务范围的不可链接的假名用于结构性隐私.
主要成果:
- 与非私人基线相比,实现了高准确度 (MAE/RMSE ~10-5).与非私人基线相比.
- 证明了快速和稳定的趋同.
- 与用户,任务和键大小显示可预测的缩放.
- 确定云端解密作为主要的计算成本.
结论:
- 个性化权重和结构性隐私为隐私关键的MCS提供了实用,高质量的聚合.
- 该框架有效地平衡了数据实用性与强大的隐私保护.
- 在不影响个人隐私的情况下,在MCS中实现可靠的数据聚合.
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