保护隐私 通过积极激励的噪音来保护分散的学习
概括
本研究介绍了积极激励噪声发生器 (PING) 和PP-DPIN用于私人去中心化学习. 这些方法提高了隐私保证和融合率,同时防御推断攻击.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 由于局部数据的敏感性,分散式学习面临隐私挑战.
- 隐私-实用性权衡阻碍了隐私保护算法的有效性.
- 密谋推断攻击对分散系统构成重大威胁.
研究的目的:
- 开发一种新的机制 (PING),以减轻隐私噪音对分散学习趋同的负面影响.
- 提出一种保护隐私的算法 (PP-DPIN),可以防御复杂的推理攻击.
- 提供可靠的隐私量化和分析去中心化学习的融合率.
主要方法:
- 引入使用网络拓和加密的积极激励噪声发生器 (PING).
- 开发PP-DPIN算法,整合差异隐私和差异信息.
- 在随机凸和非凸设置下确定收率.
主要成果:
- PING会产生相关的噪声,在防御攻击的同时保持融合.
- 对于至少一半的节点,PP-DPIN提供了强有力的隐私保证.
- 在计算机视觉任务中,相对于网络大小和卓越性能,证明了线性加快速度.
结论:
- 在分散式学习中,PING和PP-DPIN有效地解决了隐私与实用性的权衡.
- 拟议的方法提供了强大的隐私保障和更好的融合.
- 与现有方法相比,PP-DPIN显示出优越的性能和对抗攻击的稳定性.
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