对机器学习中的当前隐私方法的批评
Florian van Daalen1,2, Marine Jacquemin1, Johan van Soest1,3
1Radiation Oncology (MAASTRO) GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, Netherlands.
概括
机器学习的进步引发了隐私方面的担忧. 专注于数据主体的伤害,而不仅仅是违规行为,对于更好的数据保护和社会利益至关重要.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 伦理学 伦理学 伦理学
背景情况:
- 机器学习 (ML) 的进步是由大型数据集和物联网 (IoT) 推动的,增加了个人数据的收集.
- 数据收集的激增加剧了隐私和数据保护的担忧,例如剑桥分析公司等丑闻.
- 在ML中现有的隐私解决方案通常采用数学视角,忽视了更广泛的上下文和伦理含义.
研究的目的:
- 批判性地检查机器学习中的当前隐私方法.
- 分析大型组织如何影响数据隐私的公共话语.
- 评估当前数据保护法规的有效性.
主要方法:
- 关于机器学习中的隐私保护技术的文献综述.
- 分析公共话语和行业对数据隐私讨论的影响.
- 批评现有的数据保护法律法规.
主要成果:
- 目前对机器学习隐私的技术方法主要有利于数据控制者,而不是个人.
- 大科技组织以符合其商业利益的方式塑造隐私话语.
- 数据保护法规往往允许表面上的遵守,未能解决潜在的伦理问题.
结论:
- 建议转向以危害为中心的隐私定义,重点关注数据主体的影响.
- 重新定义隐私以优先考虑减少伤害将更好地为个人和社会服务.
- 当前的监管框架需要重新评估,以确保真正的数据主体保护.
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