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适应性隐私保护编码计算与层次任务分区.

Qicheng Zeng1, Zhaojun Nan1, Sheng Zhou1

  • 1Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

Entropy (Basel, Switzerland)
|October 25, 2024
PubMed
概括
此摘要是机器生成的。

适应性隐私保护编码计算 (APCC) 通过保护数据和减少延迟来增强分布式计算. 这种策略优化了任务恢复,并减轻了滞后效应,显著提高了效率.

关键词:
在 MINLPP 工作中,编码计算是编码的计算.层次化的任务分区 分类.保护隐私 保护隐私 保护隐私任务取消 任务取消

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科学领域:

  • 计算机科学 计算机科学
  • 信息理论 信息理论
  • 分布式系统 分布式系统

背景情况:

  • 编码计算解决了使用编码理论在分布式系统中的隐私泄漏和滞后问题.
  • 现有的方法与各种计算类型和适应性结果准确性作斗争.

研究的目的:

  • 建议适应性隐私保护编码计算 (APCC) 用于多功能计算任务.
  • 确保信息理论数据隐私并减轻滞后效应.
  • 适应性提供准确或近似的计算结果.

主要方法:

  • APCC利用编码理论来实现最佳的任务恢复和编码速度.
  • 层次任务分区和取消减轻滞后效应.
  • 混合整数非线性编程 (MINLP) 和最大值下降 (MVD) 算法解决了优化问题.

主要成果:

  • APCC保证了信息理论上的数据隐私.
  • 与基准数据相比,等级分区可以减少20.3%至47.5%的计算延迟.
  • 该MVD算法有效地解决复杂的优化问题.

结论:

  • APCC为保护隐私的编码计算提供了最佳和适应性的解决方案.
  • 该策略有效地减少了分布式环境中的任务完成延迟.
  • 与现有的最先进的方法相比,APCC显示出了显著的改进.