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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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相关实验视频

Updated: Jun 1, 2025

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
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A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

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在分割学习中基于GAN的数据重建攻击.

Bo Zeng1, Sida Luo1, Fangchao Yu1

  • 1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China.

Neural networks : the official journal of the International Neural Network Society
|January 19, 2025
PubMed
概括
此摘要是机器生成的。

在保护隐私的人工智能中使用的分割学习易受数据重建攻击的影响. 新的方法,模型近似估计重建攻击 (MAERA) 和基于蒸的客户端重建攻击 (DCRA),从服务器和客户端的角度展示了有效的攻击.

关键词:
数据重建攻击攻击数据重建攻击.分布式隐私保护机器学习生成性的对抗性网络.模型反转模型的反转.分拆学习是指分开学习.

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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相关实验视频

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科学领域:

  • 人工智能的人工智能
  • 机器学习安全 机器学习安全
  • 保护隐私的技术 保护隐私的技术

背景情况:

  • 分割学习通过将客户端和服务器之间的模型分割为隐私带来好处,适合资源有限的环境.
  • 尽管有架构保护措施,但分割学习仍然容易受到数据重建攻击的攻击,即使是部分模型访问.
  • 现有的攻击通常依赖于对攻击者能力的强有力的假设,通常是拥有全球信息的服务器.

研究的目的:

  • 在U形分割学习框架内开发和验证基于生成对抗网络 (GAN) 的数据重建攻击.
  • 调查由服务器和客户端双方发起的攻击的可行性.
  • 引入新的攻击方法,放松先前的假设,并使客户端数据重建成为可能.

主要方法:

  • 对于服务器发起的攻击,提出的模型近似估计重建攻击 (MAERA),减少对先前假设的依赖.
  • 引入了基于蒸的客户端重建攻击 (DCRA),用于新的客户端启动的数据重建.
  • 在包括CIFAR100在内的各种数据集中评估了攻击的有效性和稳定性.

主要成果:

  • 在CIFAR100上,MAERA只使用1%的测试和私人数据样本成功执行攻击.
  • DCRA展示了第一个成功的数据重建从客户端在分割学习.
  • 与传统的后期最大估计 (MAP) 估计相比,DCRA在目标类样本上显示出优异的重建效应.

结论:

  • 从服务器和客户端的角度来看,基于GAN的攻击在U形分割学习中是可行的和有效的.
  • MAERA显著降低了成功的服务器端重建攻击的要求.
  • 在分割学习框架中,DCRA为客户端隐私威胁建立了一个新的范式.