相关实验视频
Updated: May 24, 2025

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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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在线分布式凸面优化与统计隐私
概括
本研究介绍了一种保护隐私的算法,用于在多代理系统中分布式在线凸优化. 它确保了代理人的统计隐私,同时实现了竞争性的遗憾界限,平衡了隐私和绩效.
科学领域:
- 分布式系统 分布式系统
- 优化理论 优化理论
- 信息安全 信息安全
背景情况:
- 多代理系统在分布式在线受约束凸优化方面面临挑战.
- 被动对手可以通过破坏数据来损害代理人的隐私.
- 现有的方法在这种分布式环境中缺乏强大的隐私保护.
研究的目的:
- 开发一种新的算法,用于在线分布式凸优化,以保证统计隐私.
- 为了应对被动敌人的挑战,腐败代理人和推断私人信息.
- 分析预期后悔和统计隐私之间的权衡.
主要方法:
- 与全球平衡性质的相关扰动机制集成到分布式在线 (子) 梯度下降中.
- 隐私保护分布式在线凸面优化 (PP-DOCO) 算法的设计.
- 使用Kullback-Leibler分歧 (KLD) 建立隐私界限.
主要成果:
- 该PP-DOCO算法为没有腐败的代理提供统计隐私保证.
- 实现了对凸函数的O ((sqrt ((K)) 和对强烈凸函数的O ((log ((K)) 的预期后悔.
- 证明了预期的遗憾和统计隐私之间的权衡,性能与最先进的算法相匹配.
结论:
- 拟议的PP-DOCO算法有效地平衡了统计隐私和分布式在线凸优化中的预期遗憾.
- 这些发现在保护多代理系统免受被动对手的威胁方面取得了重大进展.
- 模拟结果验证了算法的有效性和观察到的隐私-遗憾权衡.
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