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科学领域:

  • 软件工程 软件工程 软件工程
  • 信息安全 信息安全
  • 数据 隐私 数据 隐私 数据

背景情况:

  • 像GDPR和CCPA这样的全球数据保护法规增加了对数据隐私的要求.
  • 在代码中识别隐私敏感信息对于合规性和安全性至关重要.

研究的目的:

  • 调查程序员识别代码中的数据敏感性的能力.
  • 评估程序员在识别敏感变量的准确性,可行性和可靠性.
  • 评估程序员之间关于数据敏感性和专业知识影响的共识.

主要方法:

  • 一个在线卡片排序实验与不同专业水平的程序员进行.
  • 参与者被要求在代码片段中感知变量名称的敏感性.
  • 分析的重点是准确性,共识,以及安全知识和专业知识的影响.

主要成果:

  • 在参与者对数据处理敏感性的看法中发现了显著的不一致性.
  • 意见的分歧随着技术专业知识的提高而增加.
  • 随着专业知识的增长,不同类别的敏感数据的共识下降.

结论:

  • 程序员在代码中的敏感数据方面表现出不同程度的隐私意识.
  • 技术专业知识的增加并不一定会导致对数据敏感性的更多共识.
  • 显然需要改进行业实践和自动化工具来识别敏感数据.