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相关概念视频

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Certain organic substances change color in dilute solution when the hydronium ion concentration reaches a particular value. For example, phenolphthalein is a colorless substance in any aqueous solution with a hydronium ion concentration greater than 5.0 × 10−9 M (pH < 8.3). In more basic solutions where the hydronium ion concentration is less than 5.0 × 10−9 M (pH > 8.3), it is red or pink. Substances such as phenolphthalein, which can be used to determine the pH of a solution, are...
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相关实验视频

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通过模型行为指标对ML预处理进行隐私保护验证.

Wenbiao Li1, Anisa Halimi2, Jaideep Vaidya3

  • 1Case Western Reserve University, Cleveland, OH 44106 USA.

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概括
此摘要是机器生成的。

我们开发了一种保护隐私的方法来验证机器学习数据的预处理. 这种方法使用模型行为分析来确保管道完整性,而不需要原始数据或标签.

关键词:
数据预处理数据的预处理.不同的隐私差异 隐私差异可以解释的人工智能AI当地差异性隐私 地方差异性隐私模型审计的审计模型.表格式数据是表格式数据.

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相关实验视频

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科学领域:

  • 机器学习 机器学习
  • 数据 隐私 数据 隐私 数据
  • 模型验证 模型验证

背景情况:

  • 确保数据预处理的完整性对于机器学习模型的可靠性至关重要,特别是对于敏感数据.
  • 现有的方法通常需要访问原始数据或标签,从而限制它们在保护隐私的场景中的适用性.

研究的目的:

  • 引入一个新的隐私保护框架,用于验证数据预处理管道的正确应用.
  • 为了使模型验证只使用黑子访问受过训练的模型,没有原始训练数据或基本真相标签.

主要方法:

  • 该框架结合了三个行为指标:预测准确度转移,输出分布的Kullback-Leibler (KL) 分歧和解释向量 (LIME/SHAP).
  • 它支持二进制正确性决定和缺失预处理步骤的多类诊断.
  • 一个没有标签的变体使用解释向量的集群进行验证.

主要成果:

  • 二进制探测器甚至在强大的局部差异隐私下 (ε=0.1) 实现了超过75%的F1得分.
  • 机器学习分类器在二进制分类任务中表现优于简单的值规则.
  • 在分类器和多类诊断门规则之间观察到可比的性能.

结论:

  • 拟议的框架提供了一个实用且可扩展的解决方案,用于保护隐私敏感机器学习中的预处理完整性.
  • 该方法有效地验证预处理管道,而不影响数据隐私或需要广泛的数据访问.
  • 没有标签的变种扩大了验证方法的适用性,以应用于缺乏标签管道示例的场景.