通过基于频谱的编码器尊重非结构化数据的部分隐私.
1School of Cyber Engineering, Xidian University, Xi'an 710126, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
机器学习即服务 (MLaaS) 用户可能会因非必需数据而泄露隐私. 这项研究引入了一种新的框架,可以从非结构化数据中过任务独立的信息,从而增强隐私保护.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 机器学习即服务 (MLaaS) 越来越受欢迎.
- 用户可能会在上传的数据中暴露敏感的,与任务无关的信息.
- 对于非结构化数据而言,现有的过任务独立信息的方法具有挑战性.
研究的目的:
- 提出一个新的框架来保护MLaaS中的用户隐私.
- 从非结构化数据中有效过任务独立信息.
- 维护与任务相关的信息对推断的有用性.
主要方法:
- 一个基于频谱的编码器将非结构化数据转化为潜在空间.
- 一个特定任务的模型识别了目标任务的基本信息.
- 该框架根据三个基准视觉数据集进行了评估.
主要成果:
- 拟议的框架为独立于任务的信息提供了更好的保护.
- 该系统有效地保持了与任务相关的信息的有用性.
- 性能与先前在基准数据集上的工作进行了比较.
结论:
- 新的框架成功地解决了MLaaS中非结构化数据的隐私泄露风险.
- 这种方法可以提高用户的隐私,而不会影响机器学习任务的有效性.
- 该方法为保护隐私的MLaaS提供了一个强大的解决方案.
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