多目标隐私保护任务分配在空间众包中的空间众包
IEEE transactions on cybernetics
|June 11, 2025
概括
这项研究引入了一个新的隐私保护任务分配框架,用于空间众包. 它增强了员工的隐私,并优化了任务质量和激励,使用分布式合作共进化的多目标模拟算法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 空间众包依赖于位置数据来分配任务,但面临隐私挑战.
- 现有的差异隐私 (DP) 方法缺乏个性化的隐私和众包的适当质量控制.
研究的目的:
- 解决当前DP空间众包框架中的局限性.
- 制定一个多目标的隐私保护任务分配 (MP-TA) 问题.
- 为了最大限度地提高员工激励和任务质量,同时确保个性化的隐私.
主要方法:
- 一个三个阶段的框架:工人提议,候选人选择和任务分配优化.
- 开发一个分布式合作共进化的多目标记忆算法 (DCC-MMA).
- 实现基于匹配的交叉,突变和修复操作以进行增强的搜索.
主要成果:
- 拟议的DCC-MMA算法为激励和质量目标提供了高质量的解决方案.
- 在解决方案质量,融合速度和可扩展性方面表现出优越性,与现有算法相比.
- 成功地将个性化的隐私保护与任务分配优化相结合.
结论:
- MP-TA 问题表述和 DCC-MMA 为保护隐私的空间众包提供了一个强大的解决方案.
- 该框架有效地平衡了工人的隐私,激励和任务质量.
- DCC-MMA显示了复杂的优化任务的效率和可扩展性的显著改善.
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